基于波形长度和自回归分析的光伏系统多尺度智能故障诊断模型

Q1 Engineering
Siti Nor Azlina M. Ghazali;Muhamad Zahim Sujod;Mohd Shawal Jadin
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引用次数: 0

摘要

非线性光伏输出受到日辐照度不均匀分布的极大影响,使传统的保护装置无法可靠地检测故障。智能故障诊断和良好的维护系统对于优化光伏系统的整体生产力和改善其生命周期至关重要。因此,提出了一种用于改进光伏系统维护策略的多尺度智能故障诊断模型。本研究的重点是诊断光伏阵列中的永久性故障(开路故障、接地故障和线路故障)和临时性故障(部分阴影),使用随机森林算法进行波形长度和自回归(RF-WLAR)的时间序列分析,并使用Matlab/Simulink进行10倍交叉验证。5.86°N和102.03°E的实际辐照度数据被用作输入,以产生与现场光伏输出数据密切匹配的模拟数据。来自马来西亚吉兰丹省Pasir Mas一座2 MW光伏发电厂维护数据库的故障数据被用于现场测试,以验证所开发的模型。RF-WLAR模型实现了98%的平均故障类型分类准确率,在对局部阴影和线路故障进行分类时准确率为100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multi-Scale Smart Fault Diagnosis Model Based on Waveform Length and Autoregressive Analysis for PV System Maintenance Strategies
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来源期刊
Chinese Journal of Electrical Engineering
Chinese Journal of Electrical Engineering Energy-Energy Engineering and Power Technology
CiteScore
7.80
自引率
0.00%
发文量
621
审稿时长
12 weeks
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