Ensemble Learning-Based Wind Turbine Fault Prediction Method with Adaptive Feature Selection

• 作者: Kaixuan Wang Mei Li Shiyao Qin Wenzhuo Wang Xiaojing Ma
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: WIND TURBINE ENSEMBLE learning FEATURE selection Support VECTOR machine SCADA data
• 年，卷（期）: 2017,（2）
• 页码范围: 134-135
• 页数: 2页
• 分类号: C5

摘要

In this paper we present a wind turbine (WT) fault detection method based on ensemble learning, WT supervisory control and data acquisition (SCADA) is used for model building. In feature selection process, random forest algorithm is applied to get the feature importances,this is much convenient compared with general feature selection by experience, also more accurate result is obtain. In model building,SVM based bagging algorithm is used, compared to individual SVM,out method is much faster and again with a better result.