摘要
针对风力发电场风力不可控、风况复杂和数据的非平稳性现状,利用风力发电场SCADA大数据,对风力发电机组进行分析,提出一种基于小波分析和神经网络的智能算法,通过分析风力发电机相关故障信号的特征,实现对风力发电机的故障诊断和预测。最后对大熊山风电场2MW 风力发电机组运行数据进行仿真和分析,仿真结果表明,小波神经网络是一种风力发电机故障诊断和预测的有效方法。
In view of the wind power uncontrollability,complex wind conditions and the non-stationary status of data in a wind farm,the SC ADA big data of wind turbine is utilized to analyze the wind turbine and a prediction method of wind turbine faults is proposed based on the wavelet neural network.Firstly,an intelligent algorithm based on the wavelet analysis and the neural network is proposed.Then,The characteristics of generator-related fault signal are analyzed to predict and diagnose the wind turbine fault based on the proposed algorithm.Finally,an example of 2 MW wind turbine generators on Damingshan Wind Farm are simulated and analyzed.It is shown that the wavelet neural network is effective for the prediction and diagnosis of wind turbines fault.
作者
肖桂雨
向健平
凌永志
何嘉平
XIAO Gui-yu;XIANG Jian-ping;LING Yong-zhi;HE Jia-ping(China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology,Wuhan 430074, China;Hunan Province 2011 Collaborative Innovation Center of Clean Energy and Smart Grid,School of Energy and Power Engineering,Changsha University of Science and Technology,Changsha 410114,China)
出处
《电力科学与技术学报》
CAS
北大核心
2019年第2期195-202,共8页
Journal of Electric Power Science And Technology
基金
湖南省科协“海智计划”(XKX-HZJH2017-06)
湖南省长沙市科技局科技计划(k1508017-11)
