李光辉;段晨东;武珊;

• 1:长安大学电子与控制工程学院

摘要(Abstract)：

由于光伏组件的非线性输出特性,传统故障保护装置难以检测到所有故障,从而导致光伏阵列出现安全问题和火灾危险。针对传统故障保护装置存在的缺陷问题,提出一种基于半监督机器学习(semi-supervised machine learning,SSML)算法的光伏阵列故障诊断模型,模型利用参考光伏组件将光伏阵列电压与电流归一化,通过半监督机器学习算法分析光伏阵列工作点状态,进而检测故障和识别故障类型。该故障诊断模型仅需要少量容易测量的标签数据,且对环境变化有较强适应能力。最后,通过仿真和实验,验证该故障诊断模型的有效性。

关键词(KeyWords)： 光伏阵列;故障检测;故障分类;半监督机器学习算法

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 李光辉;段晨东;武珊;

Email:

DOI: 10.13335/j.1000-3673.pst.2019.0695

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