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Research on Harmonic Detection System for Photovoltaic Power Grid Based on Artificial Intelligence

Published: 31 July 2024 Publication History

Abstract

Photovoltaic (PV) grid, as an important form of renewable energy, faces challenges in harmonic problems. This paper aims to study an AI-based PV grid harmonic detection system to achieve fast and accurate detection and localization of harmonics. Firstly, a mathematical model of the PV power generation system is established, and the harmonic characteristics of the PV power generation system are analyzed. Then, traditional harmonic detection algorithms and harmonic detection algorithms based on wavelet transform and FFT algorithm are discussed and simulated analyzed. On this basis, an AI-based PV grid harmonic detection system is designed, and corresponding optimization schemes are proposed. Through experimental verification, the AI algorithm demonstrates high accuracy and efficiency in PV grid harmonic detection. The research results show that the AI-based PV grid harmonic detection system can significantly improve the stability and reliability of the PV grid, providing important technical support for the practical application of PV power generation systems. Future research directions can further expand the functionality and application scope of PV grid harmonic detection systems to meet the needs of different scenarios.

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  1. Research on Harmonic Detection System for Photovoltaic Power Grid Based on Artificial Intelligence

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      cover image ACM Other conferences
      PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
      January 2024
      969 pages
      ISBN:9798400716638
      DOI:10.1145/3674225
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

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      Published: 31 July 2024

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