基于机器学习的并网光伏系统故障诊断方案

D. Singh, Laxman Solankee
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引用次数: 0

摘要

近几十年来,由于光伏技术的可及性和进步,光伏系统受到了广泛的关注。保护光伏系统免受串对串(SS)、串对地(SG)、开路(OC)和部分遮阳等故障的影响是实现成本效益和环境友好型光伏系统的关键挑战。这种不寻常的情况降低了最大可用光伏功率。因此,为了提高系统效率和可靠性,必须迅速注意到光伏阵列中的部分遮阳和故障。利用现有的安全装置,如熔断器和剩余电流检测器,可以检测出光伏系统中的重大故障电流。流动的低阶故障电流不足以使电流保护装置检测到太阳和/或故障失配是否适度而故障电阻是否高。因此,在阴天和低辐照度条件下,传统的保护装置无法识别问题,导致可靠性问题和光伏火灾威胁。在此背景下,本文提出了一种光伏系统故障诊断方案,该方案包括利用离散小波变换提取特征,利用决策树对光伏系统的各种缺陷进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MACHINE LEARNING BASED FAULT DIAGNOSIS SCHEME FOR GRID-CONNECTED PV SYSTEM
Photovoltaic (PV) systems have recieved a lot of attention in recent decades due to their accessibility and advancements in PV technology. The protection of PV systems from faults such as String to String (SS), String to Ground (SG), Open circuit (OC), and partial shading are the key challenges to the realization of cost-effective and environmentally friendly PV systems. Such unusual circumstances reduce the maximum available PV power. Partial shading and breakdowns in a PV array must therefore be noticed quickly for enhanced system efficiency and reliability. The significant fault current in PV systems can be detected using the existing safety devices in PV systems, such as fuses and residual current detectors. The flowing fault current being of low order is not significant enough for current protection devices to detect if the solar and/or fault mismatch is modest and the fault resistance is high. As a result, under cloudy and low irradiance conditions, the traditional protection devices fail to identify problems, resulting in reliability concerns and photovoltaic fire threats. In this context, a fault diagnosis scheme for PV systems is presented in this paper, which includes feature extraction using the Discrete wavelet transform, and classification of various defects on the PV system using Decision tree.
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