scholarly journals Reduced Gaussian process regression based random forest approach for fault diagnosis of wind energy conversion systems

2021 ◽  
Author(s):  
Majdi Mansouri ◽  
Radhia Fezai ◽  
Mohamed Trabelsi ◽  
Hazem Nounou ◽  
Mohamed Nounou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Wind Energy ◽  
Gaussian Process ◽  
Energy Conversion ◽  
Gaussian Process Regression ◽  
Wind Energy Conversion ◽  
Wind Energy Conversion Systems ◽  
Energy Conversion Systems

Study on Fault Diagnosis Platform in Wind Energy Conversion Systems Based on JESS

2011 ◽  
Vol 230-232 ◽  
pp. 925-929
Author(s):  
Wang Shen Hao ◽  
Feng Qin Li ◽  
Jie Han ◽  
Xin Min Dong ◽  
Hong Chen
Keyword(s):  
Fault Diagnosis ◽  
Wind Energy ◽  
Energy Conversion ◽  
Condition Monitoring ◽  
Wind Energy Conversion ◽  
Wind Generators ◽  
Wind Energy Conversion Systems ◽  
Energy Conversion Systems ◽  
Energy Conversion System ◽  
Monitoring Platform

There is a constant need for the reduction of operational and maintenance costs of Wind Energy Conversion System(WECS). The most efficient way of reducing these costs would be to continuously monitor the condition of these systems, which allows for early detection of the degeneration of the generator health, facilitating a proactive response, minimizing downtime, and maximizing productivity. Wind generators are also inaccessible since they are situated on extremely high towers.There are also plans to increase the number of offshore sites increasing the need for a remote means of WECS monitoring that overcomes some of the difficulties of accessibility problems. Therefore it is important of condition monitoring and fault diagnosis in WECS. The monitoring schemes of transfer its monitor status with JESS technology was put forward in this paper. A remote condition monitoring platform (RCMP) was designed and constructed in this project. And its result brings us an effective solution to deal with the WECS condition monitoring.

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