Data-driven Anomaly Detection Method Based on Similarities of Multiple Wind Turbines

作     者：Xiangjun Zeng Ming Yang Chen Feng Mingqiang Wang Lingqin Xia Xiangjun Zeng;Ming Yang;Chen Feng;Mingqiang Wang;Lingqin Xia

作者机构：the College of Electrical Engineering&New EnergyChina Three Gorges UniversityYichang 443002China the Key Laboratory of Power System Intelligent Dispatch and ControlShandong UniversityJinan 250061China 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2024年第12卷第3期

页      面：803-818页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 0807[工学-动力工程及工程热物理] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0835[工学-软件工程] 081002[工学-信号与信息处理] 

主　　题：Anomaly detection information entropy long short-term memory similarity assessment wind farm wind turbines 

摘      要：The operating conditions of wind turbines(WTs)in the same wind farm(WF)may share similarities due to their shared manufacturing process,control strategy,and operating ***,the similarities of WTs are seldom considered in WT anomaly detection,resulting in the disregard of useful *** paper proposes a method to improve the reliability and accuracy of WT anomaly detection using the supervisory control and data acquisition(SCADA)data of multiple WTs in the same ***,a similarity assessment method based on a comparison of different observation time series is proposed,which objectively quantifies the similarities of WT operating ***,the SCADA data of the target WT and selected WTs that are similar are used to establish several estimation models through a long short-term memory(LSTM)*** models that exhibit good estimation performance are used to construct a combined estimation model that estimates the variations in the monitored variables of the target ***,an anomaly detection method that jointly compares the effective value and information entropy of the residuals is proposed to identify *** effectiveness and accuracy of the proposed method are verified using the data of two actual WFs.