Osprey Optimization Algorithm-Based Power Quality Enhanced Grid Interfaced Green Energy Fed Electric Vehicle Charging for Industrial/House Hold Consumers
オスプレイ最適化アルゴリズムに基づく電力品質強化型グリーンエネルギー給電EV充電システム（産業・家庭用） (AI 翻訳)
K. Srilakshmi, P. Balachandran, Muhammad Ammirrul Atiqi Mohd Zainuri
🤖 gxceed AI 要約
日本語
本論文は、風力・太陽光発電とEVを統合した系統連系システムにおいて、電力品質問題（瞬断、電圧低下、高調波など）を解決するため、UPQCのパラメータ最適化にオスプレイ最適化アルゴリズム（OOA）を適用。また、レーベンバーグ・マルカート逆伝搬ニューラルネットワーク制御と新たな潮流管理アルゴリズムを導入し、THDを低減（3.58%, 1.22%, 4.36%）し、効率的な電力融通を実現した。
English
This paper applies the Osprey Optimization Algorithm (OOA) to optimize parameters of a UPQC for grid-connected systems integrating wind, solar, and EVs. It also employs Levenberg-Marquardt backpropagation neural network control and a novel power flow algorithm to address power quality issues and reduce THD (3.58%, 1.22%, 4.36%), enabling efficient power sharing among renewables, grid, EVs, and storage.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のGX文脈では、再エネ大量導入時の系統安定化やEV充電インフラの高度制御に資する技術開発として参考になる。ただし、具体的な制度・規制との連携はなく、エンジニアリング寄りの知見である。
In the global GX context
This work contributes to the global GX context by demonstrating a practical optimization framework for integrating renewable energy and EVs while maintaining power quality—a key challenge for grid decarbonization. The OOA-based approach could inform similar engineering solutions for smart grids and clean energy systems worldwide.
👥 読者別の含意
🔬研究者:Provides a novel application of Osprey Optimization Algorithm to UPQC parameter tuning, with comparative performance against GA and PSO.
🏢実務担当者:Offers a technical blueprint for designing grid-interfaced EV charging systems with improved power quality and renewable integration, applicable to industrial and residential setups.
📄 Abstract（原文）
In the current scenario, integration of clean energy sources and EVs are encouraged into the local distribution network. This research designs a UPQC by optimally selecting the gains of a FOPIDC, the inductance and resistance values of filters, and the gain parameters for the PIC of the storage battery and wind energy systems. It also optimizes the parameters of DC-DC boost and buck-boost converters using the Osprey Optimization Algorithm (OOA). Additionally, this work employs a Levenberg-Marquardt Back propagation (LMBP) neural network (NN) control for accurate reference signal generation. The main goal is to address PQ issues like interruptions, sags, harmonic distortion, swell, DC-link voltage balancing, and reduce the total harmonic distortion (THD) of the source current in a grid-connected system that integrates wind energy systems (WES), solar energy, and EVs. The study also introduces a novel power flow algorithm that manages the transfer of power between the renewable sources, grid, EVs and battery storage, thereby facilitating seamless power exchange between solar/wind/battery systems, the grid, and consumer/ industrial loads. This facilitates to ensure a consistent power supply, efficient demand fulfillment, and optimal use of generated power. The study demonstrates that the optimized UPQC, collectively with power flow management, can successfully address PQ issues and achieve efficient power sharing with reduced THD levels of 3.58%, 1.22%, and 4.36%. To exhibit the performance of the system comparison is carried out with standard genetic algorithm (GA) and particle swarm optimization (PSO) algorithms.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/tce.2025.3649064first seen 2026-05-15 19:48:41
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