Electrica is a peer-reviewed, open access, online-only journal published by İstanbul University-Cerrahpaşa. Electrica is a continuously published journal in English. The journal has moved to continuous publication by volume from Volume 25.
Image Encryption Using Quantum Logistic Mapping
With the growth of technology, the issue of safe storage and transmission of information has become even more important. Image data are increasingly used in military, business, political, economic, and other areas. The range of usage has increased, leading to increased privacy and security concerns about image data. In this study, it is aimed to present a new secure image encryption algorithm that leverages chaos theory and chaotic maps. As quantum logistic maps are sensitive to initial conditions, a new stream encryption algorithm using quantum logistic maps has been proposed to encrypt color images. In order to obtain chaotic behavior and confusion, quantum logistic map values, image values, and already encrypted values are utilized together to obtain both confidentiality and avalanche effect. Moreover, to check the efficiency of the algorithm’s performance, NIST statistical tests, histogram analysis, key sensitivity tests, correlation analysis, and information entropy tests are successfully performed. It is concluded that the proposed algorithm secures the communication among parties. Cite this article as: M. Kılıç and M.C. Kasapbaşı, “Image encryption using quantum logistic mapping,” Electrica, 25, 0059, 2025. doi: 10.5152/electrica.2025.25059.
Off-Grid Hybrid Renewable Energy System Based on BoostedSewing Algorithm and Photovoltaic-Fuel Cell
The authors optimized a hybrid photovoltaic/fuel cell system by employing the BoostedSewing algorithm, achieving significant technical and economic benefits. Experimental results demonstrate that the BoostedSewing algorithm excels in convergence speed, computational efficiency, and system performance. It effectively enhances system efficiency, reduces the levelized cost of energy, and shortens the payback period to 6.3 years. The system demonstrates excellent performance in load response, seasonal performance, and stability, particularly in controlling voltage and frequency fluctuations, ensuring high-quality and stable power supply. Compared to traditional algorithms, the BoostedSewing algorithm exhibits superior efficiency across all aspects, showcasing broad application prospects. Cite this article as: W. Du, H. Ji, H. Peng, H. Rui and J. Li, “Off-grid hybrid renewable energy system based on BoostedSewing algorithm and photovoltaic-fuel cell,” Electrica. 2025, 25, 0051, doi: 10.5152/electrica.2025.25104.
Performance Comparison of Different PV Array Configurations Considering the Number of Bypass Diodes Under Partial Shading
Partial shading is one of the most critical challenges affecting the performance and reliability of photovoltaic (PV) systems in real-world scenarios. Although bypass diodes are widely used to reduce mismatch losses in PV systems, their role is still under investigation. The effect of increasing their number on overall system performance is especially unclear for different PV array configurations. This study presents a comprehensive simulation-based analysis of how the number of bypass diodes influences the global maximum power point (GMPP) in four widely used PV array configurations: series-parallel, bridge-link, honey-comb, and total cross-tied. A MATLAB Simscape model is employed to simulate 30 shading scenarios. P-V curves are generated in order to determine the current, power, and voltage at GMPP for various bypass diode configurations, with between 6 and 24 cells per bypass diode. In this way, the individual performance of each case across different shading scenarios is compared, and the average values are computed to facilitate a comprehensive comparison of the overall performance among the so-called array configurations. The results not only reveal the relationship between array and bypass diode configurations but also offer new perspectives on how to maximize energy yield in shade-prone PV systems. Cite this article as: H. G. Sezgin-Ugranlı, “Performance comparison of different PV array configurations considering the number of bypass diodes under partial shading,” Electrica. 2025, 25, 0080 doi: 10.5152/electrica.2025.25080.
Robustness Against Adversarial Attacks Through a Hybrid Defense Approach in Medical Imaging
Medical imaging plays a vital role in clinical diagnosis, yet machine learning models used in this domain are highly vulnerable to adversarial attacks, risking misdiagnosis, which could lead to incorrect diagnoses. Using two benchmark datasets, Pneumonia and BreakHis images, this study assesses the resilience of well-known deep learning architectures, such as VGG16, ResNet50, InceptionV3, and VGG19, against adversarial attacks. For better model robustness, a hybrid defense strategy is suggested that combines adversarial training with autoencoder-based preprocessing. Results indicate that adversarial attacks degrade base model performance, but the hybrid approach enhances accuracy, precision, recall, F1 score, and area under the curve (AUC) score. Autoencoders suit BreakHis data, while adversarial training better supports Pneumonia dataset robustness. Statistical analysis and evaluation metrics such as accuracy, precision, recall, F1 Score, and AUC score on the basis of confusion matrices, and its comparison analysis visualizations support the superiority of the hybrid strategy in improving classification reliability across varying attack types. In situations with limited resources, autoencoders offer a lightweight additional defense, and adversarial training is effective on all architectures. The results demonstrate the critical need for integrated defenses in ensuring trustworthy artificial intelligence– driven medical diagnosis. Cite this article as: M. S., A. Kumar Sagar, and V. Khemchandani, “Robustness against adversarial attacks through a hybrid defense approach in medical imaging,” Electrica, 25, 0127, 2025. doi: 10.5152/electrica.2025.25127.
Improved Interleaved DC-DC Converter Without Auxiliary Switches: Analysis and Experimental Validation
This paper presents a new two-phase interleaved boost converter for photovoltaic applications. An interleaved boost converter improved with an additional resonant circuit is proposed. The auxiliary circuit with a resonant inductor, a capacitor, and two diodes is used to obtain a zero-voltage switching condition. This converter has the advantages of reduced switching losses, better efficiency, and reduced input current ripples. The operating principles and design analysis are also presented. To validate the theoretical analysis, a prototype of a 48 V/260 V/200 W converter system is developed, and an efficiency of 96% is obtained. Cite this article as: V. Venkata Subrahmanya Kumar Bhajana, P. Biswal, P. Drabek, and V. Kakani, “Improved interleaved DC-DC converter without auxiliary switches: analysis and experimental validation,” Electrica, 25, 0189, 2025. doi: 10.5152/electrica.2025.24189.
Oxidation Stability Analysis of Eco-Friendly Sunflower and Corn Oil Doped With Antioxidants
An increasing amount of attention in investigating the substitutes for hydrocarbon-based transformer liquid insulation has been observed lately. Vegetable oils have become crucial in this field, offering a chance to support developing countries’ sustainable economic progress. The main source of raw materials for the manufacturing of vegetable oils is natural feedstocks. The properties of these natural feedstocks are fundamental in characterizing the quality of vegetable oils. Interestingly, such oils often exhibit limited oxidative stability, which can cause oxidationrelated alterations in some parameters, including viscosity and acid number. Hence, antioxidants must be added in order to offset these negative effects. In this paper, among the many antioxidants, tert-butylhydroquinone (TBHQ) and butylated hydroxy toluene (BHT) are blended with two representatives of vegetable oils namely sunflower oil and corn oil. Acidity, dielectric dissipation factor, viscosity, and ultraviolet-visible spectrum are the properties analyzed at different aging durations accelerated thermally. The results clearly indicate that the antioxidants slow down the oxidation process and decrease the amount of by-products due to oxidation. Cite this article as: R. Madavan and P. Rozga, “Oxidation stability analysis of eco-friendly sunflower and corn oil doped with antioxidants,” Electrica, 25, 0077, 2025. doi: 10.5152/electrica.2025.25077.
Effects of Direct-Quadrature Rotor Current Sensors’ Faults in Wind Energy Conversion System based on Doubly Fed Induction Generator
This paper investigates the dynamic performance of a grid-connected wind energy conversion system (WECS) based on a doubly fed induction generator (DFIG) in the presence of additive and multiplicative faults in directquadrature rotor current sensors. The system architecture includes direct current-link voltage regulation, via the grid side converter and active/reactive power control, through the rotor side converter. Unlike prior studies that mainly consider symmetrical grid faults, this work systematically introduces various sensor faults specifically in stator current, stator voltage, and rotor current measurements into the rotor current control loop. The impact of these faults is assessed by examining key electrical parameters such as current, voltage, active, and reactive power across the grid, stator, and rotor. Simulation results reveal that rotor current sensor faults significantly degrade system stability and power regulation. These findings highlight the importance of developing robust fault detection and isolation and fault-tolerant control strategies and provide a foundation for enhancing the reliability of DFIG-based WECS under sensor fault conditions. Cite this article as: N. Chouider, K. Beddek, R. Haddouche, M. Zerrougui and O. Remdani, "Effects of direct-quadrature rotor current sensors’ faults in wind energy conversion system based on doubly fed induction generator," Electrica, 25, 0042, 2025. doi: 10.5152/electrica.2025.25042.
Optimum Positioning and Sizing of Biomass Distributed Generators for Real Power Transmission Congestion Management
The energy market today allows for bidirectional transactions between utilities and consumers, even within traditional practices. Factors like generator failures, maintenance of transmission lines, and high network demand can cause overheating and failures in transmission lines and equipment, resulting in contingencies within the transmission network. Methods like load shedding, rescheduling of generators, and insertion of renewable energy-based distributed generators (DGs) are various alternatives for alleviating congestion in the network lines, i.e., the transmission lines or other network elements operate at or beyond their designed capacity, restricting the flow of electricity. In this work, the insertion of renewable energy DGs at their optimal location is proposed as a novel approach for alleviating the congestion in the network lines. Firstly, the real power transmission capability distribution factors are evaluated to determine the optimal location for integrating the renewable energy DGs concerning the congested line. Furthermore, these types of DGs, which are solar, wind, and biomass, are considered along with the modeling of uncertainties in the solar and wind. The Beta and the Weibull probability distribution functions are employed to explore the uncertainty in solar and wind DGs, respectively. Furthermore, the optimal capacity of Biomass DG has been obtained by minimizing real power losses and the voltage stability margin chosen for alleviating the congestion in the network lines. The newly evolved algorithm, Grey Wolf Optimization, has been employed to solve the multi-objective optimization problem of interest. The performance of Grey Wolf - Multi-Objective Optimization is verified on a standard IEEE-30 (Institute of Electrical and Electronics Engineers) bus system to demonstrate the effectiveness of the proposed approach. Results show that the size of biomass DG and real power losses are obtained as 9.9533 MW and 9.3055 MW, respectively. Cite this article as: S. K. Warungase and M. V. Bhatkar, “Optimum positioning and sizing of biomass distributed generators for real power transmission congestion management,” Electrica, 25, 0071, 2025. doi: 10.5152/electrica.2025.24071.
Cyber-Resilient Frequency Control Design for Interlinked Alternate Current Microgrid System
Abstract: In this paper a resilient frequency control design specifically for microgrids (MGs), designed to withstand false data injection attacks (FDIA), is presented. The approach focuses on enhancing the robustness of frequency control mechanisms to ensure stable and reliable microgrid operation, even when subjected to malicious data tampering. The design integrates advanced detection and mitigation strategies to identify and counteract the impact of false data. Initially, the method integrates a bidirectional long short-term memory (BiLSTM) neural network and an improved whale optimization algorithm (IWOA) into a unified framework, working in tandem with a controller to identify and counteract FDIA . Second, a historical MG dataset containing frequency and power variations trains the BiLSTM neural network, enabling it to accurately detect multiple types of FDIAs with high precision. Lastly, the IWOA to the PID (Proportional – Integral – Derivative) controller was applied, effectively counteracting the adverse effects of FDIAs. The results demonstrate that the resilient frequency control system effectively mitigates the adverse effects of FDI attacks, maintaining MG stability and reliability. Cite this article as: D. Kumar, V. P. Singh, and M. Mallick, "Cyber-resilient frequency control design for interlinked alternate current microgrid system," Electrica, 25, 0002, 2025. doi: 10.5152/electrica.2025.25002.
Improving the Performance of Ensemble Models With Class Activation Maps
Abstract: Convolutional neural networks with different architectures extract different features. Imagine a scenario where multiple models were trained on the same dataset, with the same hyperparameters, and under the same conditions. Because of their architecture, these models will learn to extract various features. As a result, even when all conditions are identical, different architectures will achieve varying levels of test accuracy. Test accuracy can be improved even more through ensembling models with varying levels of accuracy in different ways. The purpose of this research is to examine the encoder layers of models trained in such a scenario and to compare the effects of various architectures. In the study, the CIFAR-100 dataset was used to train five models based on ResNet18, GoogleNet, and MobileNetv3L. These models were ensembled in a multitude of ways. Class activation maps were computed for both analysis and ensembling. The research demonstrated that class activation maps could be used to learn how to ensemble models. As a result, model ensembling using class activation maps significantly improved test accuracy. Furthermore, one of the most important findings from the study is that including a model with low test accuracy in the ensemble can boost overall success. Cite this article as: A. G. Gelen, “Improving the performance of ensemble models with class activation maps,” Electrica, 25, 0184, 2025. doi: 10.5152/electr ica.2025.24184.
Precision Analog Rectifiers With Current Input-Current Output Topologies: A Comprehensive Study
Abstract: Precision rectifiers play a crucial role in modern electronics, providing accurate signal processing and energy conversion for a wide range of applications. Current Input-Current Output (CI-CO) rectifiers have emerged as pivotal components in modern electronic systems, offering precise signal processing, high-speed operation, and compatibility with integrated circuit technologies. These circuits, characterized by their ability to process signals entirely in the current domain, minimize power losses and enhance system efficiency. This study provides a comprehensive analysis of CI-CO rectifier circuits, focusing on their design principles, operational advantages, and application areas. Recent advancements, including resistor-less and diode-less designs, highlight the potential of CI-CO rectifiers to address challenges in high-frequency and low-voltage applications. Particular attention is given to their role in critical fields such as biomedical instrumentation, renewable energy systems, telecommunications, and smart IoT devices, where precise current regulation and energy efficiency are paramount. By presenting a detailed chronological review and comparative analysis of existing designs, this study underscores the versatility and adaptability of CI-CO rectifiers. Additionally, key performance metrics such as power consumption, bandwidth, and harmonic distortion are evaluated to guide future research and development efforts. The findings contribute to the growing body of knowledge in analog electronics and provide practical insights for engineers and researchers seeking energy-efficient, compact, and reliable rectification solutions for next-generationapplications. Cite this article as: M. E. Başak, “Precision analog rectifiers with current input-current output topologies: A comprehensive study,” Electrica, 25, 0004, 2025. doi: 10.5152/electrica.2025.25004.
Experimental Investigation of SiC MOSFET-Based Isolated Dual Active Half-Bridge Converter: Hard-Switching Versus Soft-Switching
Abstract: This article presents the performance evaluation of an isolated dual active half-bridge (DAHB) converter with discrete SiC MOSFET devices. The impact of a resonant network on the SiC MOSFET DAHB under two different modes is investigated. The soft-switching characteristics, zero voltage switching (ZVS), and zero current switching (ZCS) operations are achieved by using a series resonant network. Another advantage of this DAHB converter without a resonant network is that it achieved partial soft switching for the main SiC MOSFETs. The discrete switching devices, SiC MOSFETs, are used to develop the DAHB module. The experimental investigations were performed on a DAHB converter system with 330V–500V switching frequency, 100 kHz operated at 700W output power, and obtained 96.5% efficiency. Cite this article as: V. Venkata Subrahmanya Kumar Bhajana, P. Biswal, P. Drabek and V. Kakani, “Experimental investigation of SiC MOSFET-based isolated dual active half-bridge converter: hard-switching versus soft-switching,” Electrica, 25, 0183, 2025. doi: 10.5152/electrica.2025.24183.
Modeling and Control of DC to High-Frequency AC Voltage Conversion for Photovoltaic Systems Under Variable Irradiation, Temperature and Load Conditions
Abstract: This study introduces a model that adjusts the DC voltage output from PV panels through a Cuk Converter while a Class-E inverter produces a high-frequency AC voltage. The model addresses the impact of varying irradiation levels on the operational conditions of the PV panels. To counteract fluctuations in DC voltage due to temperature, irradiation, or load changes, a PI Controller is utilized to keep the DC voltage constant. This stable DC voltage is then converted to AC voltage at a frequency of 15 kHz using a Class-E inverter. This modeling study aimed to maintain the inverter’s output voltage at a steady 100V (RMS), regardless of fluctuations in irradiation, temperature, and the load connected to the inverter. The modeling process involved testing four scenarios, with results analyzed for each. In the first scenario, variations in irradiation were managed effectively by the PI controller, which kept the output voltage constant. In the second scenario, when the panel temperature was modified, the PI controller ensured that the output voltage remained steady. The third scenario maintained a constant output load while altering the other two variables, and here, the PI controller successfully stabilized the output voltage as well. In the fourth scenario, all variables were changed together, dropping the output load below a critical level. As a result, the output voltage could not be kept constant due to inadequate irradiation. The study demonstrates that the model functions reliably within specific variable ranges, but instability occurs when multiple variables fall below acceptable levels simultaneously. Cite this article as: F. Issi and M. Karhan, “Modeling and control of DC to high-frequency AC voltage conversion for photovoltaic systems under variable irradiation, temperature and load conditions,” Electrica, 25, 0198, 2025. doi: 10.5152/electrica.2025.24198.
Investigating the Impact of Different Magnetic Wedges on Efficiency of Small Induction Motors
Abstract: In this study, the effects of magnetic wedges on the performance of small-power induction motors are thoroughly investigated. In order to improve motor efficiency and reduce losses, three magnetic wedges with different relative magnetic permeability values are analyzed and compared to a reference motor without a magnetic wedge. Electrical losses are computed using the Finite Element Method (FEM) in ANSYS Electronics Desktop, while thermal analysis is conducted through the FEM-based Steady-State Thermal program. The transient and steady-state losses are evaluated, and the results indicate that the use of magnetic wedges significantly enhancesmotor efficiency, primarily by reducing winding losses and minimizing temperature rise. Furthermore, magnetic wedges positively impact torque fluctuations in both transient and steady-state conditions, improving motor stability. The study confirms that the degree of efficiency improvement is directly related to the relative magnetic permeability of the wedge. Motors using a wedge with higher permeability show better performance, with a greater reduction in losses and temperature, and an improvement in torque stability. These findings show that magnetic wedges, typically used in large power motors, can also offer significant advantages in efficiency, torque stability, and temperature control when applied to small-power induction motors. Cite this article as: S. Doğan, S. Zorlu Partal and B. Gümüş, “Investigating the impact of different magnetic wedges on efficiency of small induction motors,” Electrica, 25, 0137, 2025. doi: 10.5152/electrica.2025.25137.
Multi-Perspective Prediction of Day-Ahead Electricity Spot Prices Via Canonical Cross-Covariance Analysis
Abstract: A wide range of factors affect the electricity market in different ways, including fuel prices, weather patterns, government policies, and consumer preferences. Traditional techniques, on the other hand, frequently struggle to extract underlying patterns effectively due to feature overlap, the fragility of descriptive identifiers, and the lack of a full holistic perspective. This study tackles these restrictions by using canonical correlation analysis to generate linear combinations of feature sets, allowing for the identification of strong determinants of power spot prices. This method enhances forecasting accuracy by incorporating multi-view feature extraction and multivariate analysis, providing an advantage in evaluating small changes in volatile markets. This method improves predictive accuracy and lowers forecasting mistakes by including multi-view feature extraction and multivariate analysis in the forecasting model. Thus, a significant advantage in price forecasting is gained by evaluating small changes in volatile markets as part of a whole. The proposed approach reduces underfitting issues in existing models that suffer from high dimensionality by using smooth and discriminative representations. Also, incurring long computational time attributed to matrix multiplication operations is effectively reduced. The average root mean square error of the proposed model is reduced by 14–20%. Multiple views in the embedding space also contribute to the prediction of asynchronous price movements under global and local information. Cite this article as: R. G. Birdal, “Multi-perspective prediction of day-ahead electricity spot prices via canonical cross-covariance analysis,” Electrica, 25, 0211, 2025. doi: 10.5152/electrica.2025.24211.
Beam-Squint-Aware Statistical Reconfigurable Intelligent Surface Beamforming Under Amplitude–Phase–Frequency Coupling for Dual Wideband Channels
Abstract: In this paper, the amplitude–phase–frequency coupling (APFC) constraint in practical reconfigurable intelligent surfaces (RISs) is first investigated. Building on this, three distinct beam-squint-aware and APFC-aware statistical quantized RIS designs are proposed for frequency- and spatial-wideband (dual-wideband) RIS-aided massive multiple-input multiple-output systems, optimized respectively for signal-to-noise ratio, mean statistical mutual information, and the ergodic capacity upper bound based on the cascaded channel covariance matrix. To optimize these metrics, an iterative alternating optimization (AO) algorithm, referred to as the iterative phase refinement algorithm, is adopted, which converges in just a few iterations. Simulation results show that the proposed RIS beamforming designs outperform conventional approaches in terms of spectral efficiency and outage capacity. Cite this article as: H. Özen and G. M. Güvensen, “Beam-squint-aware statistical RIS beamforming under amplitude–phase–frequency coupling for dual wideband channels,” Electrica, 25, 0154, 2025. doi: 10.5152/electrica.2025.24154.
Three-Dimensional Model Generation from Two-Dimensional Image Sequences Using Machine Learning
Abstract: In this study, a deep learning model was utilized to generate voxel-represented three-dimensional models of some objects using silhouette images of size 128 × 128 captured from four different angles. The proposed model is trained using the ShapeNet dataset. The deep learning model, along with the proposed error function, has been favored to reduce the number of parameters and capture features of different dimensions. A total of 34 691 different data were obtained in seven categories. The performance metrics of the proposed model have been compared with other studies in the literature using the Intersection over Union (IoU) metric. The comparison reveals that the proposed method achieves an IoU score of 0.5283, which outperforms both the 1 image and 5 image input versions of both McRecon and (Perspective Transformer Nets) PTN methods in categories other than the chair category. Cite this article as: M. Dağtekin, B. Aşıroğlu and Ö. C. Turna, "Three-dimensional model generation from two-dimensional image sequences using machine learning," Electrica, 25, 0014, 2025. doi: 10.5152/electrica.2025.25014.
LQR-Based PID Optimization for Model-Based Control of Brushless DC Motors in HEVs
ABSTRACT In this study, a control brushed direct current motor is selected, and a modeling study is carried out. All parameters used in the modeling are presented in detail. A controller is designed for the modeled motor. Traditional methods (Ziegler–Nichols Open Loop Response and Ziegler–Nichols Closed Loop Response), computer-aided calculation methods (MATLAB (matrix laboratory) tuning), and coefficient determination methods obtained by optimization (Linear Quadratic Regulator) are investigated to determine the parameters of the proportional integral derivative controller. The controllers obtained by these methods are applied separately to the system. The conversion of the Simulink project into code for a card with an embedded ARM (Acorn RISC Machine) processor is mentioned. How to create the project to be converted into code is explained. Motor speed and applied voltage graphs are analyzed in detail. The controller coefficients obtained with MATLAB tuning, a computer-aided calculation method, showed a high overshoot in the speed graph. Therefore, the MATLAB tuning method is not suitable for the motor control used in this study. Traditional methods are not applicable either. The controller whose parameters were generated with Ziegler–Nichols Open Loop Response showed too much speed drop when the motor was under load. The controller with parameters generated by Ziegler–Nichols Closed Loop Response has very high motor speed oscillation. The reasons why traditional methods are not applicable are explained in detail in this study. It is decided that the appropriate method for this motor is a Linear Quadratic Regulator. How to determine the controller parameters using a Linear Quadratic Regulator is explained. The applicability and comparison with other methods are presented. Cite this article as: S. İn, B. Örs and H. Tiryaki, "LQR-based PID optimization for model-based control of brushless DC motors in HEVs," Electrica, 25, 0186, 2025. doi: 10.5152/electrica.2025.24186.
Reactive Power Optimization Using Firefly Algorithm for Dispersed Electric Vehicles Charging Stations in Radial Distribution System
Abstract: Electric vehicle charging stations (EVCSs) in the distribution system are attracting more attention these days. Several technical and economic issues are associated with their management and the overall power drawn from the grid. Reactive power compensation has also been identified as a key operational consideration when using such systems. While utility-based reactive power units can be operated within the system, reactive power compensating units commissioned by EVCS owners can help address issues such as expanded line loading capacity, reserved capacity, installed capacity, and increased line losses. While optimization methods such as genetic algorithms and particle swarm optimization have been proposed, their practical implementation in largescale systems remains underexplored. As the number of EVCSs in the distribution network grows, the number of compensating units involved will increase, necessitating advanced optimization techniques to determine the appropriate rating for each EVCS at the charging station. The Firefly Algorithm, developed using MATLAB software, is employed to tackle this optimization problem. This work presents five cases that provide a line-by-line update on the proposed approach for identifying the role of reactive power compensating units in conjunction with EVCSs. The results are validated using the IEEE 14-bus and 30-bus systems. By addressing reactive power compensation challenges, the study contributes to reducing energy losses and ensuring a stable power supply. These advancements support the broader goal of sustainable energy transition and the widespread adoption of electric vehicles (EVs), ultimately fostering environmental conservation and energy equity. Cite this article as: N. K. Saxena, "Reactive power optimization using Firefly Algorithm for dispersed electric vehicles charging stations in radial distribution system" Electrica, 25, 0131, 2025. doi: 10.5152/electrica.2025.24131.
Design of Composite Repetitive Controller for Single-Phase Uninterruptible Power Supply Inverter Circuit
Abstract: Single-phase uninterruptible power supply (UPS) is widely used in all kinds of important electrical equipment to ensure the smooth implementation of power supply. The core part of the UPS is the inverter circuit, and the control of the output voltage of the inverter circuit is of great significance. This paper takes the single-phase fullbridge inverter circuit as the research object. First, the mathematical model of the system is obtained based on the frequency domain modeling method of the minimum phase system. Then, a composite controller composed of an inverse system and repetitive control is proposed. By expanding the repetitive controller transfer function, the inherent positive feedback term of the repetitive controller is found, and the inherent positive feedback term is overcome by a parallel proportional term. Using frequency domain analysis, the design process and parameter selection method of the complex repetitive controller are given. Finally, the effectiveness of the proposed control method is verified by experiments. When the inverter system is connected to the resistive load, the instantaneous output voltage error fluctuates within ±0.30 V and the total harmonic distortion rate is 1.28%. Compared with the traditional cascade control, the output voltage error fluctuates within ±1.10 V and the total harmonic distortion rate is 2.98%. The performance of the proposed control method is greatly improved, which indicates the engineering application value of the proposed inverter control scheme. Cite this article as: Y. Gangfeng and K. Xi, "Design of composite repetitive controller for single-phase uninterruptible power supply inverter circuit," Electrica, 25, 0099, 2025. doi: 10.5152/electrica.2025.24099.
Maximum PowerPoint Tracking Strategy of Photovoltaic System Based on Genetic Algorithm and Particle Swarm Optimization
Abstract: The basic genetic optimal algorithm tracks the global maximum PowerPoint but needs to work on premature convergence and limited local search, hindering optimal solution guarantees. It prematurely settles on local maxima and needs a robust local search for complex spaces. Despite some global search capacity, its flaws constrain PV conversion system maximum PowerPoint tracking (MPPT) effectiveness. Researchers thus devised an enhanced multiple-group genetic optimal algorithm for MPPT, integrating subgroup division, independent evolution, and periodic communication/sharing to address these limitations. This strategy significantly enhances the diversity of the optimization algorithm, reducing the likelihood of premature convergence. The modified perturbation genetic algorithm (MPGA) integrates the perturbation observation method for cooperative control by introducing perturbations during the evolutionary process, thereby improving the local search capability of the optimization algorithm. While the enhanced multi-group genetic optimization algorithm effectively addresses issues such as early convergence and weak local search, its algorithmic process is relatively complex. The implementation of a multi-group cooperative control mechanism boosts algorithm performance but comes with an increased computational load. This research introduces a quantum genetic algorithm for MPPT control, employing qubit encoding to achieve high precision and efficiency. Experimental results demonstrate that in a parallel system incorporating a supercapacitor storage device (SCSD), the output current remains stable at 8.6A, the output voltage reaches 29.4095V at the 6-second mark, and the power output per unit is 252.9217W, resulting in a total power of 767.8434W with a minor 2.31% decrease. Ultracapacitors effectively stabilize the power output, highlighting their essential role in SCSD components. Cite this article as: F. Bai and R. Qiao, "Maximum PowerPoint tracking strategy of photovoltaic system based on genetic algorithm and particle swarm optimization," Electrica, 25, 0139, 2025. doi: 10.5152/electrica.2025.24139.
Artificial Intelligence Meets Your Voice: Transforming Turkish Text into Personalized Speech
ABSTRACT Advancements in artificial intelligence (AI)-driven text-to-speech (TTS) technology have enabled transformative applications in accessibility and personalized learning. This study focuses on the development of a customizable Turkish TTS system aimed at enhancing lecture notes through personalized narration. Despite significant progress in widely spoken languages, the study addresses the lack of open-source Turkish TTS solutions by leveraging Narakeet TTS and fine-tuning the RVCv2 model. The methodology involves processing user-recorded voice data, segmenting audio, and training a voice conversion model to align synthesized TTS output with the speaker’s unique vocal characteristics. Metrics such as Mel-Frequency Cepstral Coefficients (MFCC), Dynamic Time Warping (DTW), Mel Cepstral Distortion (MCD), Perceptual Evaluation of Speech Quality (PESQ), and Short-Time Objective Intelligibility (STOI) were employed to evaluate system performance. In order to determine the similarity of the produced voice to the user’s voice, the first sound and the last sound were compared with the Emphasized Channel Attention, Propagation, and Aggregation Time Delay Neural Network (ECAPA-TDNN). Results demonstrate that the personalized model achieves high intelligibility and similarity to the original voice, with MCD values 0, PESQ scores 4.5, STOI scores 1, and ECAPA-TDNN similarity 0.62. This study underscores the potential of open-source TTS solutions in supporting less-resourced languages and highlights the importance of personalization in educational AI tools. Cite this article as: F. Akar, "AI meets your voice: transforming Turkish text into personalized speech," Electrica, 25, 0034, 2025. doi: 10.5152/electrica.2025.25034
Photo-Stimulable Phosphor Plate Imaging Systems and Ambient Light Exposure: Effect of Timing on Quantitative Image Quality Tests
Abstract: Photo-stimulable phosphor imaging systems can be used in daylight, but the energy stored in the plate fades with ambient light and delay. In this study, an Arduinobased experimental platform was revised to generate continuous and interrupted light. Scanning of the plates was delayed for a total of 4 minutes, while light was emitted for a total of 2 minutes. In continuous experiments, the light was applied at the beginning, middle, and end of the period, while in interrupted experiments, the light was switched on and off at a ratio of 1:3 at different time intervals. Half of the plates were protected from the ambient light during exposure. Signal-to-noise (SNR) and Contrast-to-noise (CNR) ratios were calculated using the second and fourth regions of the step-wedge. On the light side, the highest SNR values were found in the 5–15 s and the lowest in the 250–750 ms interrupted sequences. Contrast-to-noise values ranged between 23.7 and 46.7. On the dark side, the highest SNR value was found in the 250–750 ms experiment, while the lowest was calculated in the continuous light experiment with the middle sequence. Contrast-to-noise values were found between found 76.6 and 154.6. The mean SNR values were higher on the light side, while the mean CNR values were lower (p=0.02). This may indicate that the image correction algorithm results in a gain in SNR and a loss in CNR. The tool used in this research is different from other studies in terms of the modulation of LEDs and sensor tracking, and it is the first among the experiments conducted with the box by investigating the effect of different timings in the same color. This research is aimed at obtaining results in the sense of materials science, rather than an outcome that will be used directly in the clinic. Cite this article as: H. Amasya, "Photo-stimulable phosphor plate imaging systems and ambient light exposure: effect of timing on quantitative image quality tests," Electrica, 25, 0116, 2025. doi: 10.5152/electrica.2025.24116.
Monitoring and Security Assessment of Traffic in Communication Channels of Technological Corporate Computer Networks
Abstract: The article proposes a four-level generalized architecture of technological corporate computer networks (TCCNs) and assesses the elements of this architecture and information exchange via communication channels between their levels. The volume of information that is generated at the levels of sensors and technologicalprocesses, which carry out most of their activities and functions without human participationand are transmitted to higher levels, is estimated, the functions of traffic monitoring in channels are investigated, and the functions of the distribution of the information volume over time are considered. It is noted that in channels associated with sensors and technological processes, the distribution function mainly behaves as a constant function. However, traffic in communication channels associated with human activity at the upper levels of the network has a certain daily periodicity. Both cases can be used when monitoring specific channels. Formulas are given in the form of integral and discrete sum functions for determining the volume of information passing through a channel in a given time interval. Here, the average volume of information passing through a selected channel per unit of time and the peak factor of the information volume are determined. Various methods and tools are used to assess the technical condition of the TCCNs channels and their resistance to Internet threats. To address this, the article proposes a three-level backpropagation neural network. Indicators with informative features are created to determine the inputs and outputs of the neural network. Among these indicators, there is an important indicator that is formed based on the experience of the operating personnel. Five different values are formed from the impressions of the operating personnel as indicator estimates. The inputs of the neural network are mainly formed from these generated indicators. The outputs of the neural network are also determined on the basis of the impressions of the operating personnel. A network training data set is formed from the inputs and outputs of neural networks. The final result of the article is the learning algorithms based on backpropagation algorithms. Cite this article as: F. H. Pashayev, J. I. Zeynalov and H. T. Najafov, "Monitoring and security assessment of traffic in communication channels of technological corporate computer networks," Electrica, 25, 0216, 2025. doi: 10.5152/electrica.2025.24216.
Two-Cascade Modal Filters Based on Modified Microstrip Lines with Grounded Conductors at the Ends
Abstract: In this paper, two-cascade connections of three modal filters (MFs) were investigated based on modified microstrip lines (MSLs) with conductors grounded at the ends. It is an MSL with grounded conductors: one top, two symmetrical top, and two side conductors. The study included simulations in the TUSUR.EMC (TALGAT) software based on transmission line analysis and experimental validation using a P4M-18 MICRAN vector network analyzer. The results demonstrated that, due to a two-cascade connection, all three MFs can attenuate the ultrashort pulse (USP) impact by four times by decomposing it into four pulses with small amplitudes. By contrast, a one-cascade connection allows for the pulse attenuation by two times by decomposing the USP into two pulses. Meanwhile, the cross-section parameters and total length can be the same for both one-cascade and two-cascade MFs. Thus, modification by cascade connection can cost-effectively improve the protective properties of MFs against USPs. Cite this article as: I. Y. Sagiyeva, T. R. Gazizov and Y. S. Zhechev, "Two-cascade modal filters based on modified microstrip lines with grounded conductors at the ends," Electrica, 25, 0149, 2025. doi: 10.5152/electrica.2025.24149.
Maiden Application of Sinh Cosh Optimizer for Parameter Extraction of Photovoltaic Cell Models
Abstract: Environmental concerns and climate change have accelerated the worldwide transition to renewable energy sources, with solar power rising to the forefront as a leading contender thanks to its availability, affordability, and relative lack of pollution. To fully realize this potential, it is essential to conduct thorough and accurate modeling of photovoltaic (PV) systems, which requires exact parameter estimations inside PV models. In order to overcome these obstacles, this study is the first of its kind to use the newly created sinh cosh optimizer (SCHO) in parameter estimation. Achieving exact parameter extraction is greatly assisted by the SCHO, a novel metaheuristic approach. As a standard for impartiality and scientific consistency, the study employs the solar cell from RTC France. The four main types of PV systems are the single-diode (SD), double-diode (DD), three-diode (TD), and PV module series. All of these models use the same experimental methodology to include the SCHO in parameter tweaking. With the RTC France solar cell selected for the SD, DD, and TD models and the Photowatt-PWP201 for the PV module model, the SCHO has a solid foundation for evaluation thanks to its prominence in the field. Results from statistical and convergence investigations show that SCHO can reliably estimate current–voltage characteristics with minimal root mean square errors (RMSEs). One further thing that proves it works is how smoothly it converges. Through a comparison with alternative approaches, SCHO is shown to be the best at optimizing the solar PV models’ parameters, which means it has the ability to greatly increase solar energy usage. Cite this article as: S. Ekinci, "Maiden application of sinh cosh optimizer for parameter extraction of photovoltaic cell models," Electrica, 25, 0089, 2025. doi: 10.5152/ electrica.2025.24089.
Novel DSOGI-Based SRF Controller for Power Quality Enhancement in Three-Phase-Grid-Connected Dynamic Voltage Restorer Integrated With Photovoltaic System
Abstract: This paper presents a DSOGI-based SRF control methodology that regulates load voltages using a dynamic voltage restorer (DVR) with the integration of photovoltaic (PV) systems under severe grid voltage disturbances. The primary objective of the proposed controller is to provide a synchronization signal from the distorted grid voltages to the control methodology for the generation of reference signals. A DSOGI-based phase-locked loop has been employed over the conventional phase-locked loop in the proposed controller. The photovoltaic-based system has been attached to the dc link of the DVR for voltage regulation of DC link. Simulations under MATLAB/ Simulink platform are conducted to analyze controller performance under severe grid conditions such as voltage sags, voltage swells, and voltage harmonics. Moreover, controller efficiency has been verified under sudden dual voltage sags. The analysis of the PV-based DVR with the proposed controller points towards the efficiency of the proposed controller in regulating load voltages for the normal operation of consumer loads. The total harmonic distortions have been reduced significantly, complying with the IEEE standards for power quality. Cite this article as: R. Mallajoshula and I. E. S. Naidu, “Novel DSOGI-based SRF controller for power quality enhancement in three-phase-grid-connected dynamic voltage restorer integrated with photovoltaic system,” Electrica, 25, 0067, 2025. doi: 10.5152/electrica.2025.24067.
A Lossless Simulated Floating Inductor Based on the Second-Generation Current Conveyors
Abstract In this study, a new lossless simulated floating inductor (SFI) is proposed. The proposed circuit contains three noninverting second-generation current conveyors. It consists of a capacitor and three resistors, but all the passive elements are floating. The proposed SFI requires one passive element matching condition. A second-order lowpass filter (LPF) is given as an application example. All the simulations are performed through the SPICE program. Also, some experiments for the proposed SFI and second-order LPF filter are conducted. All the simulations are performed by using the macro model of the AD844, and all experimental tests are achieved by using the AD844s. Cite this article as: T. Yücehan and E. Yüce, "A lossless simulated floating inductor based on the second-generation current conveyors," Electrica, 25, 0101, 2025. doi: 10.5152/electrica.2025.24101
Asynchronous Motor Bearing Fault Diagnosis and Speed Control Using Fuzzy Logic-Based Direct Torque Controller
Abstract Asynchronous motors (ASMs) have attracted significant attention due to their extensive use in industrial applications and processes, particularly when it comes to adjusting speeds. Several speed control methods, including fuzzy logic-direct torque controller (FL-DTC), have been suggested to regulate the speed of ASMs by accurately following the desired reference speed. The fluctuating reference speed directly influences the operational frequency of the ASM stator current, a critical factor in the identification of bearing faults (BFs). Notably, BFs contribute to 40% of all ASM failures. This article describes the detection of BFs in ASMs via a combination of the Hilbert transform (HT) and the FL-DTC. Hilbert transform is employed to analyze the non-stationary nature of the stator’s current with speed variations. These variations come in two forms: those caused by the control itself and those resulting from BFs. The FL-DTC control contributes to reducing current harmonics, torque, and speed ripples. This highlights and makes the effects induced by BFs more distinct, thus rendering them more proportional. As such, the FL-DTC control is not only employed to enhance the performance of the ASM, but it is also utilized for the diagnosis of BFs. The performance of the selected approach is tested in the MATLAB/ Simulink environment. Cite this article as: A. E. Idriss, A. Derouich, S. Mahfoud, N. El Ouanjli, A. Chantoufi, and M. I. Mosaad, “Asynchronous motor bearing fault diagnosis and speed control using fuzzy logic-based direct torque controller,” Electrica, 25, 0121, 2025, doi: 10.5152/electrica.2025.24121.
Compact Antenna for Through-the-Earth Mine Communication
Abstract This research paper presents a planar Archimedean spiral antenna design for through-the-earth (TTE) mine communication. A study has been conducted to analyze the effect of factors such as depth, radial distance, loop antenna current, loop antenna radius, and conductivity of earth on TTE communication. The larger the radius of the loop antenna, the lower the frequency, the decrease in radial distance from the loop, and the increase in depth leads to a stronger magnetic field. As the loop current increases, signal strength increases. Lower conductivity allows a more extended range. Based on the study, the Archimedean spiral antenna is proposed to offer improved magnetic field strength over the conventional loop antenna while maintaining a lower thickness profile. The results demonstrate that the antenna size can be decreased without sacrificing performance by switching from a traditional loop antenna to a planar Archimedean spiral antenna. The suggested antenna offers a 50% size reduction compared to the standard single-turn loop antenna. Additionally, comparisons between the reported measured findings and the simulated outcomes are made. Cite this article as: A. K. Tiwary, R. S. Singh and V. R. Gupta, "Compact antenna for through-the-earth mine communication," Electrica, 25, 0144, 2025. doi: 10.5152/ electrica.2025.23144.
The Effect of Stator Outer Diameter and Length on the Performance of Three-Phase Induction Motor at Constant Volume
Abstract For induction motors covering the vast majority of the industry, the bulk package volume determines the amount of raw material used, i.e., its cost. In this study, the effect on motor performance is investigated by changing the stator outer diameter and motor length without changing the amount of steel sheet. While designing a low-power induction motor, other motor parameters except the motor stack length and stator outer diameter were calculated analytically, and the stator outer diameter and stack length were obtained using the surrogate optimization (SO) technique. The electric motor modeled with the obtained values was analyzed using Maxwell. In this context, the mechanical power of the designed motor was primarily determined as 1.1 kW. A three-phase induction motor is designed for the considered 1.1 kW of mechanical power. The stack package volume of this motor is calculated as 1340.89 cm3 . This motor stack package volume also indicates the amount of steel sheet to be used. Therefore, the effect on motor performance is investigated by changing the stator outer diameter and length of the motor without changing the amount of steel sheet. The number of stator and rotor poles of the motor is 36 and 28, respectively. As a result of the analysis, an efficiency value of 80.30% is obtained for the case where the Kd value, which expresses the ratio of the motor stack length to the stator outer diameter, is 0.113. Cite this article as: İ. Çelik, Y. Oğuz and M. C. Aküner, "The effect of stator outer diameter and length on the performance of three-phase induction motor at constant volume," Electrica, 25, 0124, 2025. doi: 10.5152/electrica.2025.24124.
Evaluation of Classification Performance of Honey Bee Species With Deep Learning Models
Bees, which have a critical role in the sustainability of our ecosystem, are of vital importance in maintaining ecological balance and biodiversity. More than one-third of the world’s food production is dependent on pollinators, and one of the most important of these is the honey bee. As the decline of bee populations threatens ecological balance and biodiversity, the importance of computer vision techniques for the automatic detection of these species is increasing. In this study, two different deep learning models based on classical convolutional neural network (CNN) and Residual Neural Network (ResNet) architectures are proposed and their effectiveness in classifying honey bee species is examined. Model1 is based on the CNN structure and Model2 uses CNN-ResNet integration. In the experiments on the BeeImage dataset containing images of six different honey bee species, it was determined that Model1 achieved 94.91%, and Model2 achieved 93.63% with the K-fold Cross-Validation technique. The results show that deep learning models can classify bee species in a fast and low-cost way. It is seen that these technologies have significant potential in biological research. The high success rates obtained in the study show that the developed deep learning models can contribute to the protection of biodiversity. Cite this article as: M. Çalışan and N. Olgun, “Evaluation of classification performance of honey bee species with deep learning models,” Electrica, 25, 0157, 2025. doi: 10.5152/electr.2025.24157.
Enhancing the Initial Position Estimation of a Resolver in a Servo- Motor-Controlled Satellite Ground Station With Regression Techniques
Resolvers are very accurate rotating position feedback mechanisms utilized by Low Earth Orbit (LEO) ground stations to track a satellite and communicate with it. Mechanical misalignments and temperature fluctuations are some of the environmental reasons that commonly cause resolvers to suffer from problems in calibration. The contribution of this work is to present a methodology for the calibration of 16-bit resolvers using 14 different Machine Learning (ML) techniques that improve the accuracy and reliability of LEO ground stations. Conventional calibration techniques include mechanical adjustment of the resolver for known inaccuracies. This often involves much manual refinement and recalibration to achieve any reasonable degree of accuracy. On the other hand, the proposed automatic calibration would reduce the need to routinely perform human calibration, thereby reducing wastage of time and other resources. Machine Learning statistical algorithms can learn from data and generalize to new data, including complex input-output mappings, and have made such error profiles and resolution features visible. This software-based error-compensation technique improved the target distortion ratio of a 16-bit resolver from approximately ±10% to approximately ±2%. In cases where ML is used for calibration, it is possible to reduce the goal angle error—which can reach up to 1°—to a level of 0.2°. Cite this article as: Y. Sancar and R. Görkem Birdal, "Enhancing the initial position estimation of a resolver in a servo-motor-controlled satellite ground station with regression techniques," Electrica, 25, 0167, 2025. doi:10.5152/electrica.2025.24167.
Advanced Power Quality Enhancement and Harmonic Filtering in Solar Photovoltaic Integrated Cascaded H-Bridge Inverters
Globally, grid-connected solar power systems are growing in popularity, but a significant challenge is optimizing output power while preserving excellent power quality. The depletion of fossil resources and the environmental issues surrounding traditional energy sources have led to a surge in interest in renewable energy, especially solar energy. This research suggests a hybrid control technique for enhancing the power quality of solar photovoltaic systems that are grid-connected by mitigating the effect of harmonic currents on system performance, even when linear loads are involved. The technique makes use of a 17-level cascaded H-bridge multilevel inverter that is coupled with the Puma optimizer, a bidirectional long short-term memory (BiLSTM) network, and an advanced multi-head cross-attention-based optimizer. The Puma optimizer enhances the deep learning network's learning process, while the BiLSTM network regulates the input to the cascaded H-bridge multilevel inverter to drive its output. This combination improves the efficiency of the photovoltaic generator and maximizes power extraction in the system. Simulations are performed in MATLAB to improve power quality and filter harmonics in solar photovoltaic systems for two cases: linear loads with and without fault conditions, with a constant level of radiation at 1000 W/m². The fundamental frequency of the 17-level cascaded H-bridge multilevel inverter is 50 Hz, with a magnitude of approximately 397.8. The overall harmonic distortion is kept at 3.96%. Additionally, the Puma optimizer has the lowest mean squared error (0.025). Cite this article as: V. S. Pawar and P. J. Gaidhane, "Advanced power quality enhancement and harmonic filtering in solar photovoltaic integrated cascaded H-bridge inverters," Electrica, 25, 0210, 2025. doi: 10.5152/electrica.2025.24210.
Enhancing Zero-Day Attack Detection in IoT Networks via Isolation Forest and Ensemble Tree Models
The Internet of Things (IoT) devices perform critical functions such as sensitive data collection, storage, and processing, which make them vulnerable to malicious attacks. In this study, a Network Intrusion Detection System was designed to enhance the security of IoT devices. Data sets obtained from three different IoT environments (CICEVSE2024, CICIoT2023, and RT-IoT2022) were utilized for attack detection using tree-based machine learning methods. Experimental results demonstrated that attacks were detected with an average accuracy of 99%. Additionally, a second security layer was implemented to identify zero-day attacks. Analyses showed that the Isolation Forest algorithm detected zero-day attacks with accuracies ranging from 30% to 62%. This proposed approach shows promise in enhancing security against known and unknown attacks. Cite this article as: S. Üstebay, “Improving zero-day attack detection accuracy in IoT networks with isolation forest and tree-based models,” Electrica, 25, 0177, 2025. doi: 10.5152/electrica.2025.24177.
A Hybrid Approach to Predicting Land Use/Land Cover Change: Integrating Object-Based Detection with Machine Learning
Urbanization in rapidly growing cities like Jaipur has led to significant land use and land cover (LULC) transformations, impacting natural resources and urban ecosystems. Predicting these changes accurately is critical for sustainable urban planning. This study leverages a hybrid approach combining Cellular Automata (CA) and Support Vector Machine (SVM) to model and predict LULC changes in Jaipur over the periods of 2017, 2024, and projected for 2031. Utilizing Sentinel-2 satellite data, spatial variables such as proximity to roads, central business districts, drainage, and slope were integrated to assess their influence on urban growth patterns. The results reveal that built-up areas have expanded considerably, largely replacing open and green spaces, driven by infrastructure proximity and low-slope zones. The CA-SVM model demonstrated strong predictive accuracy, with key findings suggesting continued urban sprawl that could strain local resources and reduce ecological stability by 2031. These insights highlight the value of data-driven planning strategies that can manage urban growth sustainably. Future research could enhance the model by incorporating additional socio-economic variables and employing high-resolution data for improved accuracy. The hybrid CA-SVM approach offers a robust framework for understanding and predicting LULC dynamics, providing a valuable tool for urban policymakers aiming to balance development with environmental conservation. Cite this article as: R. L. Chouhan and H. S. Shekhawat, “A hybrid approach to predicting land use/land cover change: Integrating object-based detection with machine learning,” Electrica, 25, 0181, 2025. doi: 10.5152/electrica.2025.24181.
Comparative Analysis of Metaheuristic Algorithms in PID-Based Vehicle Cruise Control Systems
This study presents a comparative analysis of five metaheuristic algorithms for optimizing Proportional Integral Derivative (PID) controller parameters in a vehicle cruise control system. The selected algorithms include the Rain Optimization Algorithm (ROA), Student Psychology-Based Optimization (SPBO), Sine Cosine Algorithm (SCA), Gradient-Based Optimization (GBO), and Grey Wolf Optimization (GWO). The PID parameters are optimized using four common performance criteria: Integral Time Absolute Error (ITAE), Integral Absolute Error (IAE), Integral Square Error (ISE), and Integral Time Square Error (ITSE). Unlike conventional tuning methods, metaheuristic approaches provide more flexible and adaptive parameter selection, enhancing the control system's robustness. The study analyses the algorithms under several internal and external disturbances, including road incline, passenger mass, and measurement noise. The findings demonstrate that no singular algorithm is universally superior; instead, efficacy is contingent upon the particular system conditions and control objectives. The Sine Conine Algorithm (SCA) has superior overall performance under typical settings, while GWO attains the quickest response time for slope fluctuations. Conversely, ROA reduces overshoot, rendering it appropriate for applications that necessitate stability. Additionally, the proposed methods are compared with the classical Ziegler-Nichols tuning approach, demonstrating the advantages of metaheuristic algorithms in terms of reduced overshoot, faster settling time, and improved robustness. The findings indicate that the SCA with the ITAE criterion yields superior performance under varying disturbance conditions. However, the fastest system response under different slope conditions is achieved with the parameter values determined by the GWO employing the ITSE criterion. The system model is designed in a MATLAB environment and the detailed comparative results are provided in figures and tables. Cite this article as: F. Heybetli, Y. Danayiyen, A. Begüm Taşdemir and Ş. Şenyiğit, "Comparative analysis of metaheuristic algorithms in PID-based vehicle cruise control systems," Electrica, 25, 0051, 2025. doi: 10.5152/electrica.2025.25051.
A Novel Acoustic Source Localization Technique for Edge AI Applications: A Lightweight Framework and Implementation for IoT and Smart Sensing Devices
This paper presents a novel and computationally efficient three-point signal estimation method for acoustic direction finding, designed specifically for low-cost embedded platforms. The proposed approach offers a lightweight alternative to traditional cross-correlation techniques by minimizing computational complexity while preserving high angular resolution. The method was implemented and tested on an STM32F429 microcontroller using a pair of MAX4466 electret microphones arranged on a fixed baseline. The system architecture leverages bare-metal signal processing routines optimized with Acorn RISC Machine Cortex. Microcontroller Software Interface Standard (ARM CMSIS-DSP) libraries, enabling real-time performance on resource-constrained hardware. Extensive experiments were conducted to evaluate the angular estimation accuracy under varying signal-tonoise ratios and source orientations. Results show that the system maintains sub-degree mean square error for source angles up to 30°, with noticeable performance degradation observed at 40° due to the directional response characteristics of the microphone elements. A comprehensive explanation is provided linking this degradation to reduced microphone sensitivity at wider angles of incidence. The proposed solution is ideal for applications requiring embedded acoustic localization, including smart interfaces, vehicular monitoring, and surveillance systems. In addition, the paper discusses the implications of deploying such systems in artificial intelligence (AI)-enabled and security-critical environments, highlighting emerging threats such as adversarial acoustic interference and spoofing attacks. These challenges underscore the importance of resilient and efficient DF methods that can operate reliably within the constraints of embedded systems. The presented work lays the foundation for future research in secure, scalable, and AI-compatible acoustic sensing platforms. Cite this article as: S. Yarkan, “A novel acoustic source localization technique for edge AI applications: A lightweight framework and implementation for IoT and smart sensing devices,” Electrica, 25, 0099, 2025. doi: 10.5152/electrica.2025.25099.
A 4-Port Semi-Circular Arc Slotted MIMO Antenna for Sub-6 GHz 5G Automotive Wireless Communications
An antenna is designed in a multi-input multi-output configuration composed of four radiating elements embedded with semi-circular cuts for sub-6 GHz 5G automotive applications. The performance of the proposed design has been enhanced with the utilization of the concept of partial ground. The antenna covers the −10 dB impedance bandwidth in the frequency spectrum of sub-6 GHz, which includes 0.9 GHz, 3 GHz, and 4.7 GHz. The proposed design exhibits a wide bandwidth of 4.8 GHz, ranging from 0.7 GHz to 5.5 GHz with a peak gain of 8.8 dB in the resonating band. The isolation between the elements is well below −15 dB, and to maintain this high isolation, the antenna elements are placed at an optimized distance from each other with independent partial ground planes. The proposed design is also fabricated, and the measured results have been compared with those obtained in a simulated environment. It has been observed that both results are quite promising and well-comparable. The size of the proposed design is about (28 × 28) cm2 as the first resonating frequency is 0.9 GHz. Nevertheless, the proposed design is suitable to integrate on the rooftop of the modern passenger cars with 5G automotive wireless applications. Cite this article as: A. Kumar, B. Chandra Sahoo, G. Singh, S. Ranjan Mishra, A. Sharma, and R. Kumar, “A 4-port semi-circular arc slotted MIMO antenna for sub-6 GHz 5G automotive wireless communications,” Electrica, 25, 0150, 2025. doi: 10.5152/electrica.2025.24150.
Artificial Intelligence for IT Operations–Based Performance Optimization in Desktop Virtualization
Gradient Boosting and XGBoost achieved the lowest root mean squared error (RMSE, ≤ 3.9 MHz) and the highest R-squared (R², ≥ 0.74), whereas SVR and KNN underperformed, likely due to data sparsity and high dimensionality. Gradient Boosting and XGBoost achieved the lowest root mean squared error (RMSE, ≤ 3.9 MHz) and the highest R-squared (R², ≥ 0.74), whereas SVR and KNN underperformed, likely due to data sparsity and high dimensionality. This study investigates artificial intelligence for IT operations (AIOps)–based performance optimization for virtual desktop infrastructure (VDI) by integrating automatic resource allocation, dynamic load balancing, and real-time performance monitoring.Using production telemetry from a corporate VDI cluster, we trained and compared seven machine-learning models XGBoost, Gradient Boosting, Random Forest, LightGBM, support vector regression (SVR), k-nearest neighbors (KNN), and Decision Tree to predict central processing unit (CPU), memory, and response-time dynamics. Gradient Boosting and XGBoost achieved the lowest root mean squared error (RMSE, ≤ 3.9 MHz) and the highest R-squared (R², ≥ 0.74), whereas SVR and KNN underperformed, likely due to data sparsity and high dimensionality. .The proposed AIOps pipeline reduces mean response time by 27%, memory consumption by 18%, and halves manual incident tickets, enabling proactive capacity management. These findings demonstrate the practical viability of AIOps for holistic, real-time VDI performance governance. Cite this article as: M. A. Özdemir and H. Özcan, "Artificial intelligence for IT operation–based performance optimization in desktop virtualization," Electrica, 2025, 25, 0032, doi: 10.5152/electrica.2025.25032.
Design and Simulation Analysis of Source-Grid-Load-Storage Integrated Scheduling for Large-Scale Power Systems
With the rapid development of new power systems and global energy transformation, the comprehensive scheduling difficulty of large-scale power systems continues to increase. The research aims to design a comprehensive scheduling model to optimize the balance between energy supply and demand, efficiently utilizing electricity. This study establishes an energy control hub and performs random fuzzy power flow calculations. The alternating direction multiplier algorithm is used for regional decentralized calculation in threelayer scheduling programming. Then, a source-grid-load-storage integrated scheduling model based on the alternating direction multiplier algorithm is designed. According to the verification results, the designed model had the lowest abandoned wind and solar rates, which were 7.4% and 2.6%, respectively. During peak shaving periods, the frequency remained stable at around 49.72 Hz, and the demand response at the load side helped reduce frequency drops. The overall cost was 11.7% lower than that of the large-scale power system scheduling model based on Benders. The results indicate that the scheduling model designed for large-scale power systems can flexibly and efficiently integrate source-grid-load-storage scheduling, achieving efficient energy utilization and supply-demand balance. The research designs a new solution for the efficient and flexible operation of largescale power systems. Cite this article as: Q. Luo, Y. Wu, C. Zhou, and D. Zhu, “Design and simulation analysis of source-grid-load-storage integrated scheduling for large-scale power systems,” Electrica, 25, 0138, 2025. doi: 10.5152/electrica.2025.24138.
Performance Comparision of Busbar and Cable Based Electrical Network Systems
Nowadays, with the increase of high powered devices, there has been a significant increase in energy consumption. In order to meet the consumption demands and with the increase in the number of high-rise buildings, components such as cables and busbars are effectively used in supply networks. Busbars are frequently used power system elements compared to traditional cable systems that are prefabricated as modular for the transmission and distribution of electrical energy. In this study, classical cable systems and busbars are examined under a specified load, and their electrical performances are provided. Electrical parameters such as power, voltage drop, and THD (total harmonic distortion) of equivalent busbars and cables under load are obtained in terms of load capacity. Electromagnetic analyses are performed and the electromagnetic emission of these systems are analyzed. In addition, the temperatures caused by the increasing currents in the busbars and cables due to the specified loads are determined with a thermal camera. In addition, two operational buildings with similar power values and conductor quantities are examined. These busbar- and cable distribution-based buildings are compared in terms of voltage drop for certain distribution line lengths. In this study, experimental results for both cable and busbar systems are presented and comparisons are interpreted to select the optimum network type. The results suggest that busbars are preferable in high-current applications where reduced heating and voltage drops are critical, while cables may be more suitable in environments sensitive to electromagnetic interference. Cite this article as: C. Burak Fırat, S. Cekli and C. Polat Uzunoğlu, "Performance comparision of busbar- and cable-based electrical network systems," Electrica, 25, 0094, 2025. doi: 10.5152/electrica.2025.25094.
Techno-Economic and Environmental Assessment of On-Grid and Off-Grid Renewable Energy Systems for Çanakkale, Türkiye Using HOMER Pro
The increasing depletion of fossil fuel resources and their detrimental impact on the environment have heightened the demand for renewable energy sources in the power generation sector. Renewable sources are favored for their sustainability, reliability, and eco-friendliness. This study evaluates the technical and economic viability of both on-grid and off-grid hybrid renewable energy systems for Çanakkale, Turkey, using HOMER Pro software. The analysis incorporates solar photovoltaic (PV) panels, wind turbines, and storage components, assessing key metrics such as Net Present Cost (NPC), Cost of Energy (COE), Operating Cost (OC), and emissions. Results reveal that the on-grid system has an NPC of $25.1M, COE of $0.0257/kWh, and OC of $3.33M, while the off-grid system’s NPC is $9.88M, COE $0.371/kWh, and OC $106.1M. The off-grid system reduces carbon emissions by 99.3% compared to the on-grid system but at higher costs. Annual electricity production for the on-grid system is 77.886.964 kWh, with 69.8% from wind turbines, 29.5% from PV, and 0.8% from the grid. The off-grid system generates 7.813.622 kWh annually, predominantly from PV (76.6%). The findings highlight a trade-off between economic feasibility and environmental sustainability, with the on-grid system offering cost advantages and the off-grid system excelling in emission reductions. This study aids in strategic decision-making for renewable energy investments. Cite this article as: N. Pamuk and F. A. Omar, “Techno-economic and environmental assessment of on-grid and off-grid renewable energy systems for Çanakkale, Türkiye, using HOMER Pro,” Electrica, 25, 0118, 2025. doi: 10.5152/electr.2025.25118.
Enhanced Edge Data Security Scheme for Smart Grids Based on Federated Learning and Blockchain
The authors propose a data security enhancement scheme for smart grids based on Federated Learning and Blockchain to address issues of data privacy leakage and tampering in edge computing environments within smart grids. In edge computing scenarios, traditional centralized model training is vulnerable to attacks and struggles to protect data privacy, with an attack success rate as high as 54.3%. By integrating Federated Learning and Blockchain technologies, the authors have constructed a secure architecture that enables the system to protect users’ electricity consumption data privacy without sharing raw data, while ensuring the integrity and security of the data transmission process. Experimental results show that the privacy leakage rate of the Federated Learning+Blockchain scheme is only 0.9%, compared to 60.2% for schemes without privacy protection. Additionally, the Federated Learning+Blockchain approach outperforms traditional solutions in terms of data transmission, processing time, Central Processing Unit (CPU) and memory consumption, and security, achieving a balance between performance and security. Cite this article as: J. Wang, J. Xu, J. Li, C. Xu and H. Xie, "Enhanced edge data security scheme for smart grids based on federated learning and blockchain," Electrica 25, 0102, 2025. doi: 10.5152/electrica.2025.25102.
Cognitive Electronic Warfare Application to Emitter Identification Process in Airborne Radar Warning Receiver Systems
Cognitive electronic warfare (CEW) will soon replace conventional electronic warfare (EW) systems in the operational area. Although the CEW concept is utilized on various platforms, it has primarily emerged in military air platforms. Radar warning receiver (RWR) applications are essential in the CEW concept. Effective RWR systems accurately identify radar emitters in complex and dynamic environments, which is critical for situational awareness of air vehicle systems. systems provide information to countermeasure systems, increasing operational success by neutralizing enemy radar and defense systems. To sense the threats and apply appropriate countermeasures, such as jamming or dispensing chaff, requires a quick response and accurate RWR processes. RWR processes are summarized as receiving, signal detection, deinterleaving, labeling, and emitter identification. This study investigates the application of machine learning (ML) techniques to the emitter identification process. The research demonstrates significant improvements in emitter identification accuracy and distance determination by using ML algorithms. The findings suggest that CEW strategies incorporating adaptive learning models can enhance RWR system performance in identifying and categorizing emitters, even in dense Radio frequency (RF) environments. In the trials, it is observed that while the decision tree, ensemble bagged, and support vector machines achieved 100% success in the classification step, the Gaussian method was the most successful, with a mean absolute percent error of 1.6 in the distance calculation step using regression. These results suggest a promising direction for developing more intelligent and resilient EW systems. Cite this article as: İ. Çetin Taş and A. G. Pakfiliz, “Cognitive electronic warfare application to emitter identification process in airborne radar warning receiver systems,” Electrica, 2025, 25, 0182, doi: 10.5152/electrica.2025.24182.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0/
Assessing Ampacity Performance Through Dynamic Line Rating: A Comparison of IEEE Std 738 and CIGRÉ TB 601
Overhead transmission lines (TLs) are typically rated using static methods that assume conservative environmental conditions, such as high ambient temperatures and low wind speeds. These conservative ratings often result in underutilization of the line’s full ampacity, as extreme conditions occur only intermittently. Dynamic line rating (DLR) addresses this issue by utilizing real-time weather and grid load data to dynamically calculate the available ampacity, considering favorable environmental conditions. The DLR continuously updates ampacity calculations, allowing operators to access unused capacity when conditions are more favorable. This approach improves transmission efficiency and enhances grid reliability, ensuring a more adaptive and resilient power system. By integrating real-time weather data, DLR provides a more accurate representation of a TL’s actual capacity than static ratings. Both IEEE and CIGRÉ standards employ the heat balance equation to calculate ampacity, accounting for heat absorption and dissipation. This paper compares the methodologies outlined in IEEE Std 738 and CIGRÉ TB 601, focusing on their approaches to calculating conductor temperature and ampacity. The study examines the impact of different modeling approaches on ampacity calculations and the performance of TLs. The analysis shows significant differences between the two methods. In summer, the IEEE method increases ampacity by 47.2% compared to static line rating (SLR), while the CIGRÉ method increases it by 46.5%. In winter, the IEEE method shows a 36.9% increase, and CIGRÉ shows a 38.6% increase. These results demonstrate the potential of DLR to optimize transmission capacity and improve grid performance by adapting to real-time environmental conditions. Seasonal variations further highlight how factors like temperature and wind speed impact ampacity, reinforcing the value of DLR systems for maximizing TL efficiency year-round. Cite this article as: Z. Zia and C. F. Kumru, "Assessing ampacity performance through dynamic line rating: A comparison of IEEE std 738 and CIGRÉ TB 601," Electrica, 2025, 25, 0109, doi: 10.5152/electrica.2025.25109.
Blockchain Based Ownership and Domain Name System Configuration With Ethereum Rollups
Cite this article as: F. Asgarov, F. S. Duran, N. Samadov and Ş. Bahtiyar, “Blockchain based ownership and DNS configuration with ethereum rollups,” Electrica, 25, 0051, doi: 10.5152/electrica.2025.25005.
Design of a Dual-Layer Capacity Configuration Model for Hybrid Energy Storage in Distribution Networks With Photovoltaic Power Sources
The traditional distribution network has significant shortcomings in terms of distributed power access capability, automation level, and operational efficiency, making it difficult to meet the development needs of modern power systems. To this end, an innovative photovoltaic power (PP) and hybrid energy storage (ES) collaborative configuration model is proposed, which significantly improves the performance and economy of the distribution network by introducing a lithium battery super-capacitor hybrid ES system and a double-layer capacity optimization framework. This model adopts a dynamic service pricing mechanism in the upper level optimization to minimize the operating costs of the power grid. In the lower level optimization, the dynamic allocation of ES charging and discharging power is used to achieve the lowest operating costs of the PP station, and efficient decision-making is achieved using the IBM ILOG CPLEX Optimization Studio (CPLEX) solver. The experimental results show that the model achieves charging efficiencies of 90.1% and 92.3%, discharge efficiencies of 91.2% and 93.5%, and response times of 2.9 seconds and 3.1 seconds, respectively, in the tests of A and B locations. The performance is significantly better than traditional pumped storage and single ES schemes. Meanwhile, the output power curve of the model exhibits minimal fluctuations, effectively suppressing the intermittent effects of PP generation. Under both dynamic and fixed electricity pricing mechanisms, the model can intelligently guide ES to discharge at full power during peak load periods. The peak valley difference under dynamic electricity pricing is reduced by 219.8 kW, and the net income is increased to 112987.1 yuan, which is 42.9% higher than the fixed electricity pricing mechanism. Through the collaborative optimization of photovoltaic-hybrid ES and double-layer capacity configuration, the study not only solves the stability and economic problems of the distribution network but also provides a reliable technical solution for the high proportion of new energy access. Cite this article as: X. Huang, D. Xu, X. Qin and X. Tao, “Design of a dual-layer capacity configuration model for hybrid energy storage in distribution networks with photovoltaic power sources,” Electrica, 25, 0038, 2025. doi: 10.5152/electrica.2025.25038.
Optimization of Istanbul Urban Traffic Data with Grey Wolf Optimization-Assisted Machine Learning Techniques: Comparative Analysis of Long Short-Term Memory and eXtreme Gradient Boosting
This study examines machine learning approaches enhanced by the Grey Wolf Optimization (GWO) algorithm for analyzing Istanbul’s complex urban traffic patterns. The GWO was employed due to its capability to efficiently handle complex nonlinear traffic patterns and its robustness in achieving global optimization, outperforming several conventional metaheuristics. Using hourly traffic density data from June–December 2024 obtained from Turkey’s Ministry of Environment database, researchers developed a Unified Traffic Density Index combining average speed (40%), vehicle count (40%), and speed variation (20%) to identify the busiest traffic zones. The dataset was obtained from the Ministry of Environment, Urbanization, and Climate Change and covers the period from June 1, 2024, to December 31, 2024. The dataset contains hourly data for 2462 geohash regions within the boundaries of Istanbul province. The methodology compared long short-term memory (LSTM) and eXtreme Gradient Boosting (XGBoost) algorithms using both standard and GWO-optimized hyperparameters. Time series analysis separated trend, seasonality, and randomness components while examining hourly and daily periodicity patterns in traffic data. Results demonstrated that GWO optimization significantly enhanced both algorithms’ performance. The standard LSTM model’s systematic deviations and wave-like patterns were substantially reduced through GWO optimization. The XGBoost performed consistently in both versions, with the GWO-XGBoost combination achieving superior prediction accuracy. Performance metrics revealed that GWO-XGBoost attained the lowest mean squared error (1.6209) and mean absolute error (0.9082) values while achieving the highest coefficient of determination (R2 percentage bias = ±0.8486%, outperforming other configurations. These findings indicate that the GWO-XGBoost combination shows significant potential as a highly accurate solution for traffic density prediction applications for traffic management systems within critical high-density zones of metropolitan areas like Istanbul, particularly for traffic density prediction applications. The study concludes that advanced optimization techniques are essential for addressing traffic management challenges in rapidly urbanizing cities with increasing vehicle density. Cite this article as: N. Subaşi, "Optimization of Istanbul urban traffic data with Grey wolf optimization-assisted machine learning techniques: comparative analysis of long short-term memory and eXtreme gradient boosting," Electrica, 25, 0196, 2025. doi:10.5152/electrica.2025.25196.
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