唐明珠 匡子杰 吴华伟 胡嘉豪 毛学魁 彭巨

摘   要：针对风力发电机组变桨系统故障诊断模型参数难以优化问题，提出了基于状态转移算法优化多类最优间隔分布机（multi-class Optimal Margin Distribution Machine optimized by the State Transition Algorithm，mcODM-STA）的风电机组变桨系统故障诊断方法. 该方法选择风电机组功率输出作为主要状态参数，利用Pearson相关系数对风电数据采集与监视控制系统中风电机组历史运行数据进行相关性分析，剔除与功率输出状态参数相关性较低的特征，对余下特征进行二次分析，减少样本特征. 将数据集分为训练集和测试集，训练集用来训练所提故障诊断模型，测试集用来进行测试. 利用国内风電场实际运行数据进行实验验证. 实验结果表明，与其他多种参数优化方法相比，所提方法故障诊断准确率和Kappa系数更高.

关键词：多类最优间隔分布机;状态转移算法;故障检测;风电机组;SCADA 系统;进化算法

中图分类号：TK83                                 文献标志码：A

Fault Diagnosis of Wind Turbine Pitch System Based on Multi-class Optimal

Margin Distribution Machine Optimized by State Transition Algorithm

TANG Mingzhu1，KUANG Zijie1，WU Huawei2?，HU Jiahao1?，MAO Xuekui3，PENG Ju4

（1. School of Energy and Power Engineering，Changsha University of Science & Technology， Changsha 410114，China;

2. Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle，Hubei University of

Arts and Science，Xiangyang 441053，China;3. State Grid Beijing Haidian Electric Power Supply Company，Beijing 100195，China;

4. Inner Mongolia Qingdianyun Power Service Co，Ltd，Baotou 014030，China）

Abstract：Aiming at the problem that the parameters of fault diagnosis model are difficult to be optimized of wind turbine pitch system， a fault diagnosis method of wind turbine pitch system based on multi-class optimal margin distribution machine optimized by the state transition algorithm （mcODM-STA） is proposed. In this method， the wind turbine power output is selected as the main state parameter， and Pearson correlation coefficient is used to analyze the historical operation data of wind turbine in wind power data acquisition and monitoring control system， and the features with low correlation of power output state parameters are eliminated. The remaining features are analyzed twice to reduce the sample features. The data set is divided into training set and test set. The training set is used to train the proposed fault diagnosis model， and the test set is used for testing. The operation data of a domestic wind farm is used for experimental verification. Experimental results show that the proposed method has higher fault diagnosis accuracy and Kappa coefficient than other parameter optimization methods.

Key words：multi-class optimal margin distribution machine;  state transition algorithm;fault detection;wind turbines;SCADA systems;evolutionary algorithms

风力发电机组通常运行在复杂多变的不稳定自然环境中，常年受到阳光、雨水、风沙等侵蚀，存在许多故障隐患，其主要零部件运行于高空，一旦风电机组因故障而引起长时间停机，将严重影响发电量和花费大量成本来维护检修及更换零件，引起巨大的经济损失[1].

变桨距系统是风电机组中的重要部分，及时有效地对变桨系统进行状态监测和故障诊断具有重要意义. 近年来基于机器学习的风电机组故障诊断方法获得广泛应用，利用风电数据采集与监视控制系……