Electricity, Volume 6, Issue 2 (June 2025) – 9 articles

This paper presents a comprehensive analysis of the effects of noise on the detection and localization of faults in transmission lines compensated with a unified power flow controller using traveling wave-based methods. This study focuses on the impact of harmonic and transient noises, which are inherent to power generation, transmission, and UPFC operation. A novel algorithm is proposed combining the Discrete Wavelet Transform and Clarke Transform to detect and localize faults under various noise conditions. The algorithm is tested on a simulated transmission line model in MATLAB/Simulink (Version R2022b) with noise levels of 20 dB, 30 dB, and 40 dB and transient frequencies of 1 kHz, 5 kHz, and 10 kHz. The results demonstrate that the algorithm achieves an average fault localization error of 0.523% under harmonic noise and 0.777% under transient noise, with fault detection rates of 96.3% and 90.75%, respectively. This study highlights the robustness of the traveling wave method in noisy environments and provides insights into the challenges posed by UPFC-compensated lines. Full article

This study focuses on the experimental determination of the lumped parameters of a Spoke-Type Axial-Flux Permanent Magnet (STAFPM) motor. This type of motor offers high specific torque and is well-suited for transportation applications. The studied STAFPM motor uses Ferrite magnets, which are more environmentally friendly and economical than rare earth magnets. The identification of the lumped electromechanical model parameters is carried out using static torque measurements on a dedicated test bench. The torque measurements are performed in two stages: with and without magnets mounted in the rotor. The no-load flux is determined separately by no-load tests. Together, these tests identify the key parameters of the lumped parameter model, such as self- and mutual inductances, cogging torque, and no-load flux. These parameters are then used to complement the DQ model, commonly used in electric motor analysis. While the DQ model predicts average torque well, it cannot reproduce torque ripples. The lumped parameter model, validated by three-phase DC testing, provides an accurate representation of the motor’s behavior, including torque ripples. This study also applies Maximum Torque Per Ampere (MTPA) control strategies and offers a practical alternative to 3D Finite Element Analysis (FEA), thus aiding the design of STAFPM motors. Full article

The successful integration of photovoltaic (PV) generators in cities requires careful planning that accounts for possible factors influencing their operation. Numerous authors have extensively studied these factors; however, the urban environment has its unique characteristics. This study aims to conduct a narrative review of the most common and influential urban factors that impact the operation of PV modules and explore potential mitigation strategies. Based on preliminary knowledge on the topic, a methodology was proposed according to which they are classified into two categories: those enhanced by the urban environment and those specific to it. A total of 97 studies, mostly from the last decade, were selected based on the relevance and impact criteria. Shading, soiling, and snow were analyzed in an urban context, followed by different urban-specific factors, such as the urban landscape, pollution, and the limitations of PV mounting spots, which can lead to more than 50% performance losses. The performed review also identified the key and most promising approaches for mitigation of the abovementioned factors, such as electrostatic dust cleaning and forward bias current snow removal. Furthermore, recommendations for urban landscape planning were made in the context of PV integration. This review could also be useful for designers and operators of urban PV facilities by providing them with basic guidelines for their optimization. Full article

The rapid proliferation of renewable energy sources (RESs) has significantly reduced system inertia, thereby intensifying stability challenges in modern power grids. To address these issues, this study proposes a comprehensive approach to improve the grid stability concerning RESs and load disturbances. The methodology integrates controlled energy storage systems, including ultra-capacitors (UC), superconducting magnetic energy storage (SMES), and battery storage, alongside a robust frequency regulation management system (FRMS). Central to this strategy is the implementation of a novel controller which combines a constant with proportional–integral–derivative (PID) and modified fractional-order (MFO) control, forming 1+MFOPID controller. The controller parameters are optimized using a novel formulation of an improved objective function that incorporates both frequency and time domain characteristics to achieve superior performance. The efficacy of the proposed controller is validated by comparing its performance with conventional PID and fractional-order PID controllers. System stability is further analyzed using eigenvector analysis. Additionally, this study evaluates the performance of various energy storage systems and their individual contributions to frequency regulation, with a particular emphasis on the synergistic benefits of battery storage in conjunction with other storages. Finally, sensitivity analysis is conducted to assess the impact of parameter uncertainties in the system design, reinforcing the robustness of the proposed approach. Full article

This paper discusses the operation and control of a low-voltage DC (LVDC) isolated distribution network powered by distributed generation (DG) from a variable-speed wind turbine induction generator (WTIG) to supply unbalanced AC loads. The system incorporates a DC-DC storage converter to regulate network voltages and interconnect battery energy storage with the DC network. The wind turbines are equipped with a squirrel cage induction generator (IG) to connect a DC network via individual power inverters (WTIG inverters). Loads are unbalanced ACs and are interfaced using transformerless power inverters, referred to as load inverters. The DC-DC converter is equipped with a novel control strategy, utilizing a droop regulator for the DC voltage to stabilize network operation. The control system is modeled based on Clark and Park transformations and is developed for the load inverters to provide balanced AC voltage despite unbalanced load conditions. The system employs the perturbation and observation (P&O) method for maximum power point tracking (MPPT) to optimize wind energy utilization, while blade angle controllers maintain generator performance within rated power and speed limits under high wind conditions. System operation is analyzed under two scenarios: normal operation with varying wind speeds and the effects of load variations. Simulation results using PSCAD/EMTDC demonstrate that the proposed LVDC isolated distribution network (DC) achieves a stable DC bus voltage within ±5% of the nominal value, efficiently delivers balanced AC voltages with unbalanced levels below 2%, and operates with over 90% wind energy utilization during varying wind speeds, confirming LVDC network reliability and robustness. Full article

This research evaluates the economic feasibility of implementing smart metering (SM) systems in Peruvian electricity distribution companies, prioritizing the maximization of the benefit–cost ratio (BCR). Seven communication architectures were analyzed in four companies, considering variables such as energy losses, meter costs, and per capita consumption. The results, evaluated through economic indicators such as the net present value, internal rate of return (IRR), and BCR showed that Luz Del Sur (LDS) obtained the best results, while ADINELSA (an electrical infrastructure management company), Sociedad Eléctrica Sur Oeste (SEAL), and Electro Sur Este (ELSE) presented the worst. The combination of power line communication and general packet radio service was the most viable architecture, followed by radio frequency mesh. However, this study concludes that a massive deployment of SM in Peru is not yet economically viable because of low per capita consumption and high meter costs. Future research should consider the benefits of distributed generation and demand management, as well as evaluate new communication technologies. Full article

Power distribution systems frequently encounter various fault-causing events. Thus, prompt and accurate fault diagnosis is crucial for maintaining system stability and safety. This study presents an innovative residual block-convolutional block attention module-convolutional neural network (ResBlock-CBAM-CNN)-based method for fault cause diagnosis. To enhance diagnostic precision further, the proposed approach incorporates a multimodal data fusion model. This model combines raw on-site measurements, processed data, and external environmental information to extract relevant fault-related details. Empirical results show that the ResBlock-CBAM-CNN method, with data fusion, outperforms existing techniques significantly in fault identification accuracy. Additionally, t-SNE visualization of fault data validates the effectiveness of this approach. Unlike studies that rely on simulated datasets, this research uses real-world measurements, highlighting the practical applicability and value of the proposed model for fault cause diagnosis in power distribution systems. Full article

The allocation and sizing of electrical substations are critical for the efficient planning and expansion of distribution networks. This study presents the application of an enhanced Black Widow Algorithm (BWA) to solve this complex problem, considering multiple variables and constraints. The BWA, inspired by the reproductive behavior of black widows, was employed to optimize the placement and sizing of new substations and connection of load centers. Two mutation methods were evaluated: the original BWA mutation and a genetic algorithm-inspired mutation incorporated into the BWA algorithm (GA mutation). Four scenarios with varying load center distributions were tested to assess the algorithm’s adaptability and performance. The results showed that the GA mutation consistently outperformed the original mutation in more complex scenarios, reducing total costs by up to 14.75%. The proposed GA mutation enabled greater flexibility in escaping local optima, leading to improved solutions in scenarios involving numerous new load centers. Additionally, increasing the number of generations and black widows enhanced convergence and solution stability, particularly in challenging cases. This study demonstrates the feasibility of using the enhanced BWA for real-world applications, offering a valuable tool for electrical distribution planning. Full article

This paper introduces an alternative forecasting approach that leverages the application of visibility graphs in the context of multivariate energy forecasting for a regional airport, which incorporates energy demand of diverse types of buildings and wind power generation. The motivation for this research stems from the urgent need to enhance the accuracy and reliability of load forecasting in microgrids, which is crucial for optimizing energy management, integrating renewable sources, and reducing operational costs, thereby contributing to more sustainable and efficient energy systems. The proposed methodology employs visibility graph transformations, the superposed random walk method, and temporal decay adjustments, where more recent observations are weighted more significantly to predict the next time step in the data set. The results indicate that the proposed method exhibits satisfactory performance relative to comparison models such as Exponential smoothing, ARIMA, Light Gradient Boosting Machine and CNN-LSTM. The proposed method shows improved performance in forecasting energy consumption for both stationary and highly variable time series, with SMAPE and NMRSE values typically in the range of 4–10% and 5–20%, respectively, and an $R^2$ reaching 0.96. The proposed method affords notable benefits to the forecasting of energy demand, offering a versatile tool for various kinds of structures and types of energy production in a microgrid. This study lays the groundwork for further research and real-world applications within this field by enhancing both the theoretical and practical aspects of time series forecasting, including load forecasting. Full article