光伏系统
计算机科学
支持向量机
粒子群优化
断层(地质)
电弧故障断路器
逆变器
风力发电
模式识别(心理学)
算法
人工智能
电压
工程类
电气工程
短路
地震学
地质学
作者
Xuerong Cai,Rong‐Jong Wai
出处
期刊:IEEE Journal of Photovoltaics
日期:2022-07-01
卷期号:12 (4): 1058-1077
被引量:23
标识
DOI:10.1109/jphotov.2022.3166919
摘要
In a solar photovoltaic (PV) power generation system, arc faults including series arc fault (SAF) and parallel arc fault (PAF) may occur due to aging of joints or other reasons. It may lead to a major safety accident, such as fire, if the high temperature caused by the continuous arc fault is not identified and solved in time. Because the SAF without drastic current change is difficult to detect, an intelligent detection algorithm based on the optimized variational mode decomposition and the support vector machine (SVM) is investigated in this article. The proposed algorithm uses the variational mode decomposition to extract the fault information from current signals, and then screens the statistical information of the signals in each frequency band by the proposed adaptive feature screening. The features to be strongly correlated with classification are taken as inputs into the SVM optimized by the particle swarm optimization for classification finally. The proposed intelligent framework not only can accurately identify the SAF occurring at different locations, but also identify the PAF. Moreover, it can also maintain good diagnosing results under the occurrence of dynamic shading, inverter startup, and SAF under wind blowing. In addition, single-series PV string and solar PV power generation systems in different countries are also used to examine the universal ability of the proposed algorithm. As for experimental results, the detection accuracy is more than 98.21% under all examined conditions.
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