基于 PSO 优化核主元分析的海上风电机组运行工况分类.pdfVIP

第 44 卷 第 16 期 电力系统保护与控制 Vol.44 No.16 2016年 8月16日 Power System Protection and Control Aug. 16, 2016 DOI: 10.7667/PSPC151450 基于 PSO 优化核主元分析的海上风电机组运行工况分类 郑小霞 1，李美娜 1，王 靖 2，任浩翰 2，符 杨 1 (1.上海电力学院自动化工程学院，上海 200090；2.上海东海风力发电有限公司，上海 200090) 摘要：海上风电机组运行环境复杂多变，对其工况进行分类可以提高机组运行健康状态评价的准确性，为制定合 理的运行维护策略提供可靠依据。提出一种基于 PSO 优化核主元分析(KPCA)的多参数工况分类方法。针对核函 数参数难以确定的问题，综合考虑类内散度和类间散度构建优化核参数的适应度函数，应用 PSO 算法对其进行寻 优，将优化后的 KPCA 用于数据的特征提取，在此基础上采用模糊 C-均值聚类(FCM)建立分类模型。通过对 UCI 数据库中的三组实验数据进行分类验证了该方法的有效性。最后，应用该方法对某海上风电场实测数据进行工况 分类，并与 PCA+FCM、KPCA+FCM 两种方法进行比较。结果表明，提出方法的分类结果优于其他两种，能够得 到清晰准确的分类结果，利于分工况建立准确的机组运行健康状态评价模型。 关键词：海上风电机组；工况分类；PSO；核主元分析；类别可分性；模糊 C-均值聚类 Operational conditions classification of offshore wind turbines based on kernel principal analysis optimized by PSO ZHENG Xiaoxia1, LI Meina1, WANG Jing2, REN Haohan2, FU Yang1 (1. School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China; 2. Shanghai Donghai Wind Power Co., Ltd., Shanghai 200090, China) Abstract: As considering the complex operational conditions of offshore wind turbines, classifying the operational conditions can improve the accuracy of health condition evaluation of wind turbines and provide a reliable basis for the reasonable operation maintenance strategies. An operational conditions classification method based on kernel principal analysis (KPCA) optimized by particle swarm optimization algorithm (PSO) is proposed. The fitness function of kernel function parameter optimizations is constructed by considering within class scatter and between-class scatter of data to avoid the problem that choosing a proper kernel function parameter is difficult. The KPCA optimized by PSO is applied to data feature extraction and a classification model is built by using fuzzy C-means (FCM) clustering algorithm. The simulations on three groups data of UCI database prove the methods