基于GOA‑SVM的光伏阵列故障诊断方法研究  

Research on fault diagnosis method for photovoltaic array based on GOA-SVM

作　　者：杨帅 曾文伟 杨凌云 黄瑞 刘谋海 易钦逸 高云鹏[4] YANG Shuai;ZENG Wenwei;YANG Lingyun;HUANG Rui;LIU Mouhai;YI Qinyi;GAO Yunpeng(Power Supply Service Center,State Grid Hunan Electric Power Co.,Ltd.,Changsha 410004,China;Hunan Province Key Laboratory of Intelligent Electrical Measurement and Application Technology,Changsha 410004,China;State Grid Hunan Electric Power Co.,Ltd.,Changsha 410004,China;College of Electrical and Information Engineering,Hunan University,Changsha 410082,China)

机构地区：[1]国网湖南省电力有限公司供电服务中心,湖南长沙410004 [2]智能电气量测与应用技术湖南省重点实验室,湖南长沙410004 [3]国网湖南省电力有限公司,湖南长沙410004 [4]湖南大学电气与信息工程学院,湖南长沙410082

出　　处：《电力科学与技术学报》2024年第5期172-180,共9页Journal of Electric Power Science And Technology

基　　金：国家重点研发计划(2021YFF0602402)。

摘　　要：光伏阵列输出功率随机性、波动性强。如果其发生故障,将严重影响电力系统安全与稳定。针对当前光伏故障诊断的准确率低和收敛速度慢的难题,提出一种基于蝗虫算法-支持向量机(grasshopper optimization algorithm‐support vector machine,GOA‐SVM)模型的光伏阵列故障诊断方法。首先,建立光伏阵列等效电路模型,分析光伏阵列的伏安曲线变化特性;其次,考虑环境影响因素和光伏阵列规模非线性变化,提取反映不同故障特性的特征量,将数据映射到高维空间进行非线性处理;最后,提出蝗虫算法(grasshopper optimization algorithm,GOA)优化非线性支持向量机改进方法,建立GOA‐SVM光伏阵列故障诊断模型,并结合实例进行仿真。研究结果表明:该方法可应用于多种不同规模的光伏阵列模型,且均能实现对光伏阵列故障的有效诊断,其对4×3光伏阵列规模的数据仿真分类准确率可达99.8088%。采用美国国家标准与技术研究院(National Institute of Standards and Technology,NIST)公开数据集进行验证,其故障诊断准确率达到92.3682%。与其他方法相比,该方法的召回率及F1‐Score均有明显提升。The output power of photovoltaic(PV)arrays exhibits strong randomness and volatility.In the event of a fault,it can severely impact the safety and stable operation of the power system.Addressing the challenges of low accuracy and slow convergence in current PV fault diagnosis,this paper proposes a PV array fault diagnosis method based on the grasshopper optimization algorithm-support vector machine(GOA-SVM)model.Firstly,an equivalent circuit model of the PV array is established to analyze the variation characteristics of the PV array's voltage-current curve.Secondly,considering environmental factors and the nonlinear changes in the scale of the PV array,feature quantities reflecting different fault characteristics are extracted,and the data is mapped into a high-dimensional space for nonlinear processing.Finally,an improved method for optimizing the nonlinear support vector machine using GOA is proposed,and a GOA-SVM PV array fault diagnosis model is established,with simulations conducted using practical examples.The research results indicate that this method can be applied to various PV array models of different scales and effectively diagnose faults in PV arrays.For a 4×3 PV array scale,the data simulation classification accuracy can reach 99.8088%.When validated using the publicly available dataset from the national institute of standards and technology(NIST),the fault diagnosis accuracy achieves 92.3682%.Compared with other methods,this approach demonstrates significant improvements in recall rate and F1-Score.

关 键 词：光伏阵列 故障诊断 多分类 最优超平面 GOA‐SVM模型 

分 类 号：TM615[电气工程—电力系统及自动化]