基于KPCA方法的储能电站故障诊断研究  

Research on Fault Diagnosis of Energy Storage Stations Based on KPCA Method

作　　者：孙昂 袁杰 王云钢 SUN Ang;YUAN Jie;WANG Yungang(Sungrow Power Supply Co.,Ltd.,Hefei,Anhui 230088,China;Offshore Oil Engineering Co.,Ltd.,Tianjin 300461,China;Petrochemical Branch of Shandong Haihua Group Co.,Ltd.,Weifang,Shandong 262737,China)

机构地区：[1]阳光电源股份有限公司,安徽合肥230088 [2]海洋石油工程股份有限公司,天津300461 [3]山东海化集团有限公司石油化工分公司,山东潍坊262737

出　　处：《自动化应用》2025年第7期141-143,共3页Automation Application

摘　　要：作为现代电力系统中的关键组成部分,储能电站的可靠运行对电力系统的稳定性至关重要。随着储能技术的广泛应用,储能电站故障诊断成为一项关键技术工作。传统的主成分分析(PCA)方法被广泛应用于故障诊断领域,但其主要提取系统运行中的线性特征,难以有效应对储能电站数据中的非线性成分。基于核主成分分析(KPCA)方法,利用能源管理系统(EMS)收集储能电站运行数据,分析并识别电站的运行故障。通过与传统PCA方法的对比实验,验证了KPCA方法在处理数据非线性故障特征时的优越性。As a key component of modern power systems,the reliable operation of energy storage stations is crucial for the stability of the power system.With the widespread application of energy storage technology,fault diagnosis of energy storage power stations has become a key technical task.The traditional Principal Component Analysis(PCA)method is widely used in the field of fault diagnosis,but it mainly extracts linear features during system operation and is difficult to effectively deal with nonlinear components in energy storage power plant data.Based on the method of Kernel Principal Component Analysis(KPCA),the Energy Management System(EMS)is used to collect operational data of energy storage power plants,analyze and identify operational faults of the power plants.Through comparative experiments with traditional PCA methods,the superiority of KPCA method in processing nonlinear fault features of data has been verified.

关 键 词：储能电站 故障诊断 主成分分析 核主成分分析 非线性 

分 类 号：TP306.3[自动化与计算机技术—计算机系统结构]