The Transactions of the Korean Institute of Power Electronics (전력전자학회논문지)

The Korean Institute of Power Electronics (전력전자학회)
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Comparative Study of Artificial-Intelligence-based Methods to Track the Global Maximum Power Point of a Photovoltaic Generation System

태양광 발전 시스템의 전역 최대 발전전력 추종을 위한 인공지능 기반 기법 비교 연구

  • Lee, Chaeeun (Dept. of Electrical Engineering, Hanyang University) ;
  • Jang, Yohan (Dept. of Electrical Engineering, Hanyang University) ;
  • Choung, Seunghoon (Dept. of Electrical & Electronic Engineering, Yonam Institute of Technology) ;
  • Bae, Sungwoo (Dept. of Electrical Engineering, Hanyang University)
  • Received : 2021.10.07
  • Accepted : 2022.02.21
  • Published : 2022.08.20
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Abstract

This study compares the performance of artificial intelligence (AI)-based maximum power point tracking (MPPT) methods under partial shading conditions in a photovoltaic generation system. Although many studies on AI-based MPPT have been conducted, few studies comparing the tracking performance of various AI-based global MPPT methods seem to exist in the literature. Therefore, this study compares four representative AI-based global MPPT methods including fuzzy logic control (FLC), particle swarm optimization (PSO), grey wolf optimization (GWO), and genetic algorithm (GA). Each method is theoretically analyzed in detail and compared through simulation studies with MATLAB/Simulink under the same conditions. Based on the results of performance comparison, PSO, GWO, and GA successfully tracked the global maximum power point. In particular, the tracking speed of GA was the fastest among the investigated methods under the given conditions.

Keywords

Acknowledgement

본 연구는 산업통상자원부(MOTIE)와 한국에너지기술평가원(KETEP)의 지원을 받아 수행한 연구과제입니다. (No. 20192010107050)

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