文章基本信息

标题：Enhanced Gaussian Process Regression for Diagnosing Wind Energy Conversion Systems
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作者：Majdi Mansouri ; Radhia Fezai ; Mohamed Trabelsi 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：6
页码：673-678
DOI：10.1016/j.ifacol.2022.07.205
语种：English
出版社：Elsevier
摘要：AbstractFault detection and diagnosis techniques are increasingly important to ensure robust and resource efficient operation of Wind Energy Conversion (WEC) systems. In this context, this paper presents a Reduced Enhanced Gaussian Process Regression (REGPR)-based Random Forest (RF) technique (REGPR-RF) to identify and diagnose faults occurring in a nonlinear WEC systems. The proposed technique uses REGPR technique for features extraction and selection from raw sensor data. Then, these selected features are fed to RF classifier to reliably detect and classify faults. The use of REGPR to learn features avoid the dimension problems and improves the classification performance significantly with a small number of training data. The results obtained by REGPR-RF are compared to those obtained with other conventional classifiers (Support Vector Machines (SVM), Naive Bayes (NB), ...). The results show that the developed REGPR-RF technique achieve higher accuracy (99.99%) with small data sets.
关键词：KeywordsGaussian Process Regression (GPR)Noise-Signal-DependenceRandom Forest (RF)Feature ExtractionSelectionFault DetectionDiagnosis (FDD)Wind Energy Conversion (WEC) Systems