Power Curve Modeling for Wind Turbine Using Hybrid-driven Outlier Detection Method

作     者：Qi Yao Yang Hu Jizhen Liu Tianyang Zhao Xiao Qi Shanxun Sun Qi Yao;Yang Hu;Jizhen Liu;Tianyang Zhao;Xiao Qi;Shanxun Sun

作者机构：Energy and Electricity Research CenterJinan UniversityZhuhaiChina School of Control and Computer EngineeringNorth China Electric Power UniversityBeijingChina 

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

年 卷 期：2023年第11卷第4期

页      面：1115-1125页

核心收录：

学科分类：080801[工学-电机与电器] 0808[工学-电气工程] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Guangdong Basic and Applied Basic Research Foundation(No.2020A1515110547) Open Fund of State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems(China Electric Power Research Institute)(No.NYB51202101982)。 

主　　题：Wind turbine power curve modeling outlier detection data-driven expert system 

摘      要：Wind power curve modeling is essential in the analysis and control of wind turbines(WTs),and data preprocessing is a critical step in accurate curve modeling.As traditional methods do not sufficiently consider WT models,this paper proposes a new data cleaning method for wind power curve modeling.In this method,a model-data hybrid-driven(MDHD)outlier detection method is constructed,and an adaptive update rule for major parameters in the detection algorithm is designed based on the WT model.Simultaneously,because the MDHD outlier detection method considers multiple types of operating data of WTs,anomaly detection results require further analysis.Accordingly,an expert system is developed in which a knowledgebase and an inference engine are designed based on the coupling relationships of different operating data.Finally,abnormal data are eliminated and the power curve modeling is completed.The proposed and traditional methods are compared in numerical cases,and the superiority of the proposed method is demonstrated.