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.

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 energyinvestments. 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.

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.

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.

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.

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.

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 asgenetic 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.

ABSTRACTIn 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.

Abstract: In this paper, two-cascade connections of three modal filters (MFs) were investigated based on modifiedmicrostrip lines (MSLs) with conductors grounded at the ends. It is an MSL with grounded conductors: one top, twosymmetrical top, and two side conductors. The study included simulations in the TUSUR.EMC (TALGAT) softwarebased 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-cascadeconnection 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 theends," Electrica, 25, 0149, 2025. doi: 10.5152/electrica.2025.24149.

Abstract: 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 SquareError (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 isachieved 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 cruisecontrol systems," Electrica, 25, 0051, 2025. doi: 10.5152/electrica.2025.24051.