基于SCADA数据分析和稀疏自编码神经网络的风电机组在线运行状态监测  

ONLINE CONDITION MONITORING FOR WIND TURBINES BASED ON SCADA DATA ANALYSIS AND SPARSE AUTO-ENCODER NEURAL NETWORK

作　　者：金晓航[1,2,3] 许壮伟 孙毅 单继宏[1,2] Jin Xiaohang;Xu Zhuangwei;Sun Yi;Shan Jihong(Key Laboratory of Special Purpose Equipment and Advanced Processing Technology,Ministry of Education and Zhejiang Province,Zhejiang University of Technology,Hangzhou 310023,China;College of Mechanical Engineering,Zhejiang University of Technology,Hangzhou 310023,China;Ninghai ZJUT Academy of Science and Technology,Ninghai 315600,China;Institute of Ocean Research,Zhejiang University of Technology,Hangzhou 310014,China)

机构地区：[1]浙江工业大学特种装备制造与先进加工技术教育部重点实验室,杭州310023 [2]浙江工业大学机械工程学院,杭州310023 [3]宁海县浙工大科学技术研究院,宁海315600 [4]浙江工业大学海洋研究院,杭州310014

出　　处：《太阳能学报》2021年第6期321-328,共8页Acta Energiae Solaris Sinica

基　　金：国家自然科学基金(51505424,51675484);浙江省自然科学基金(LY15E050019);浙江省公益技术应用高新项目(LGG19F010008);宁波市自然科学基金(2018A610045)。

摘　　要：通过融合稀疏自编码器和深度神经网络算法,提出一种基于SCADA数据的风电机组在线运行状态监测方法。首先,通过稀疏自编码器学习SCADA高维数据中复杂的内在特征,得到数据的降维表示;其次,基于降维后的数据利用深度神经网络预测风电机组的有功功率,通过对比分析预测功率与实际功率之间的残差判断风电机组的运行状态;最后,利用某风电机组近一年半的SCADA数据,对所提方法进行验证分析,结果表明,所提方法提早5天检测出风电机组发电机的异常情况,为有效避免故障恶化引发的突然停机、降低运维成本、提高风电能源的竞争力提供技术支持和保障。An integrated approach,which is based on SCADA data analysis,sparse self-encoder and deep neural network algorithms,is proposed for wind turbines online condition monitoring.Firstly,the complex intrinsic features of SCADA high-dimensional data are learned by sparse auto-encoder,and the reduced dimension data is obtained.Secondly,deep neural network is used to predict the output power of wind turbine based on the reduced dimension data,wind turbine’s condition is judged by analyzing the residuals between the predicted active power and the actual active power.Finally,SCADA data of a wind turbine for nearly one and a half years are used to verify the proposed method.Results show that the proposed approach can detect anomalies of wind turbine generator 5 days before it is shut down for maintenance which can avoid the shutdown caused by catastrophic failures,reduce the maintenance cost,and improve the competitiveness of the wind energy.

关 键 词：风电机组 状态监测 深度神经网络 稀疏自编码器 数据采集与监控系统 

分 类 号：TH17[机械工程—机械制造及自动化]