Electric Power Components and Systems

Papers
(The H4-Index of Electric Power Components and Systems is 16. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-05-01 to 2025-05-01.)
ArticleCitations
Power Quality Disturbances Segmentation: An Approach Based on Gramian Angular Field71
Transmission Line Fault Localization With Mesh and Surface Analysis Using PCA Features53
Study of a Spoke-Type Ferrite Structure as an Alternative to Surface-Mounted NdFeB PMSGs: A Performance Comparison Based on Getting the Same Efficiency42
Multi-Source Collaborative Control Technology of Photovoltaic Power Generation Based on Differential Evolution-Gray Wolf Optimization Algorithm38
Iot and AI-Based MPPT Techniques for Hybrid Solar and Fuel Cell35
Emperor Penguin Optimization Based MPPT for PV System under Different Irradiation Condition28
Terminal Voltage and Common Mode Voltage Analysis for Various PV Inverter Topologies21
Power Management Systems for Harmonic Elimination Using Hybrid Artificial Neural Networks Approaches21
An Enhanced Hybrid Rhino Herd–PSO Optimizer for Optimal Technical and Economic Operation of Power Systems Considering Environmental Concerns21
Electromagnetic Force Distribution of Planar Coils: Analytical Model and Optimization20
Multi-Agent Systems Based Adaptive Protection for Smart Distribution Network19
Smart Short-Term Load Forecasting through Coordination of LSTM-Based Models and Feature Engineering Methods during the COVID-19 Pandemic17
EVs Owner Benefit Evaluation Through Energy Exchange in the Smart Radial Distribution Network17
Probabilistic Transition-Based Optimal Energy Transaction of a Non-Convex CHP-Microgrid during Generation and Load Uncertainty17
Analytical MPPT Control and Comparative Analysis for PV Panel Connected to DC Microgrid17
Power Control and Optimization for Power Loss Reduction Using Deep Learning in Microgrid Systems17
DeepFore: A Deep Reinforcement Learning Approach for Power Forecasting in Renewable Energy Systems16
Hybrid Deep Learning-Based Grid-Supportive Renewable Energy Systems for Maximizing Power Generation Using Optimum Sizing16
Optimal Micro-PMUs Placement with Channel Limits using Dynamically Controlled Taguchi Binary Particle Swarm Optimization16
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