Clustering Wind Turbines for SCADA Data-Based Fault Detection
Author:
Affiliation:
1. University of Tokyo, Bunkyo-ku, Tokyo, Japan
2. Research Center for Advanced Science and Technology, University of Tokyo, Meguro-ku, Tokyo, Japan
3. Eurus Technical Service Corporation, Tokyo, Japan
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Renewable Energy, Sustainability and the Environment
Link
http://xplorestaging.ieee.org/ielx7/5165391/9994274/09925095.pdf?arnumber=9925095
Reference30 articles.
1. Use of SCADA Data for Failure Detection in Wind Turbines
2. A Data-Driven Approach for Monitoring Blade Pitch Faults in Wind Turbines
3. A Data-Driven Residual-Based Method for Fault Diagnosis and Isolation in Wind Turbines
4. Wind Turbine Gearbox Failure Identification With Deep Neural Networks
5. A Multi-Fault Detection Method With Improved Triplet Loss Based on Hard Sample Mining
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