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

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

机构地区：[1]the College of Electrical Engineering&New Energy,China Three Gorges University,Yichang 443002,China [2]the Key Laboratory of Power System Intelligent Dispatch and Control,Shandong University,Jinan 250061,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2024年第3期803-818,共16页现代电力系统与清洁能源学报（英文）

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 environment.However,the similarities of WTs are seldom considered in WT anomaly detection,resulting in the disregard of useful information.This 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 WF.First,a similarity assessment method based on a comparison of different observation time series is proposed,which objectively quantifies the similarities of WT operating conditions.Then,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)algorithm.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 WT.Finally,an anomaly detection method that jointly compares the effective value and information entropy of the residuals is proposed to identify anomalies.The effectiveness and accuracy of the proposed method are verified using the data of two actual WFs.

关 键 词：Anomaly detection information entropy long short-term memory similarity assessment wind farm wind turbines 

分 类 号：TM614[电气工程—电力系统及自动化] TP274[自动化与计算机技术—检测技术与自动化装置]