Quantile regression neural network‐based fault detection scheme for wind turbines with application to monitoring a bearing
Author:
Affiliation:
1. School of ManagementHefei University of Technology Hefei China
2. Key Laboratory of Process Optimization and Intelligent Decision‐MakingMinistry of Education Hefei China
3. Anhui Ronds Science & Technology Incorporated Company Hefei China
Funder
Innovative Research Group Project of the National Natural Science Foundation of China
Publisher
Wiley
Subject
Renewable Energy, Sustainability and the Environment
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/we.2375
Reference36 articles.
1. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description
2. Detection and classification of faults in pitch-regulated wind turbine generators using normal behaviour models based on performance curves
3. Condition monitoring and prognosis of utility scale wind turbines
4. Monitoring of wind farms’ power curves using machine learning techniques
5. Wind Turbine Condition Assessment Through Power Curve Copula Modeling
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