摘要
基于风电机组数据采集与监控系统(SCADA)的大量时序数据分析,提出一种基于生成对抗网络(GAN)的风电机组在线状态监测方法。首先,通过GAN模型中的生成器获得一组与SCADA数据相同维度的生成数据;其次,利用生成的和真实的SCADA数据对GAN模型进行优化训练进而得到用于区分机组健康状态的判别器模型。利用所提方法分别对一台故障风电机组和一台健康风电机组的SCADA数据分析后发现:GAN方法能够有效监测风电机组的在线运行状态,比SCADA系统提早5 d发现故障机组的异常;当风电机组正常工作时,GAN方法比其他方法(如马氏距离、主成分分析、深度神经网络、支持向量机等)误报的次数更少;当机组发生故障后,GAN方法比上述其他方法能检测出更多的异常样本。
Based on the analysis of large amounts of time series data collected by supervisory control and data acquisition system(SCADA) in wind turbines, a wind turbine online condition monitoring approach based on generative adversarial network(GAN) is proposed. Firstly, a data set that has the same dimension with the SCADA data is generated with the generative model. Secondly, the generated SCADA data and the real SCADA data are used to optimize and train the GAN model. After training, the obtained discriminative model in GAN is used for distinguishing the health condition of wind turbines. Finally, the proposed approach was used to analyze the SCADA data of a healthy and a faulty wind turbines. The result shows that the GAN-based approach can effectively monitor the online operation condition of the wind turbines, it can detect the anomalies of the faulty wind turbine 5 days earlier than the SCADA system. When the wind turbine works normally, the number of false alarms reported by the GAN approach is less than other approaches(such as Mahalanobis distance, principal component analysis, deep neural network and support vector machine). When the wind turbine fails, the GAN-based approach can detect more abnormal samples than other approaches.
作者
金晓航
许壮伟
孙毅
单继宏
王欣
Jin Xiaohang;Xu Zhuangwei;Sun Yi;Shan Jihong;Wang Xin(l.Key Laboratory of E&M,MOE,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;Zhejiang Windey Co.,Ltd.,Hangzhou 310012,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2020年第4期68-76,共9页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(51505424,51675484)
浙江省自然科学基金(LY15E050019)
宁波市自然科学基金(2018A610045)
浙江省大学生科技创新活动计划(新苗人才计划)项目资助。
关键词
风电机组
数据采集与监控系统
生成对抗网络
状态监测
wind turbine
supervisory control and data acquisition system(SCADA)
generative adversarial network
condition monitoring
