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。
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 method＇s validity. Finally, the method is used to classify the operational conditions of offshore wind turbines, and the results show that the proposed method can get much better classification effect than PCA＋FCM and KPCA＋FCM. It is helpful for establishing health condition evaluation model for each condition.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2016年第16期28-35,共8页
Power System Protection and Control
基金
国家自然科学基金项目(51507098)
上海绿色能源并网工程技术研究中心(13DZ2251900)
上海市科委重点科技攻关项目(14DZ1200905)
上海市电站自动化技术重点实验室项目(13DZ2273800)~~
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
offshore wind turbines
operational conditions classification
PSO
kernel principal analysis
sort separability criterion
fuzzy C-means clustering
