scholarly journals Operational-Condition-Independent Criteria Dedicated to Monitoring Wind Turbine Generators

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Wenxian Yang ◽  
Shuangwen Sheng ◽  
Richard Court
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Operational Conditions ◽  
Turbine Generators ◽  
Wind Turbine Generators ◽  
Monitoring Technique ◽  
Electrical Faults ◽  
The Time Domain ◽  
Offshore Plants ◽  
Potential Tool

Condition monitoring is beneficial to the wind industry for both land-based and offshore plants. However, because of the variations in operational conditions, its potential has not been fully explored. There is a need to develop an operational-condition-independent condition monitoring technique, which has motivated the research presented here. In this paper, three operational-condition-independent criteria are developed. The criteria accomplish the condition monitoring by analyzing the wind turbine electrical signals in the time domain. Therefore, they are simple to calculate and ideal for online use. All proposed criteria were tested through both simulated and practical experiments, showing that these criteria not only provide a solution for detecting both mechanical and electrical faults that occur in wind turbine generators, but that they provide a potential tool for diagnosing generator winding faults.

scholarly journals An End-to-End, Real-Time Solution for Condition Monitoring of Wind Turbine Generators

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4817
Author(s):  
Adrian Stetco ◽  
Juan Melecio Ramirez ◽  
Anees Mohammed ◽  
Siniša Djurović ◽  
Goran Nenadic ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Real Time ◽  
Condition Monitoring ◽  
Sampling Rate ◽  
Dynamic Visualization ◽  
Turbine Generators ◽  
Wind Turbine Generators ◽  
Multi Stage ◽  
Expert Analysis ◽  
End To End

Data-driven wind generator condition monitoring systems largely rely on multi-stage processing involving feature selection and extraction followed by supervised learning. These stages require expert analysis, are potentially error-prone and do not generalize well between applications. In this paper, we introduce a collection of end-to-end Convolutional Neural Networks for advanced condition monitoring of wind turbine generators. End-to-end models have the benefit of utilizing raw, unstructured signals to make predictions about the parameters of interest. This feature makes it easier to scale an existing collection of models to new predictive tasks (e.g., new failure types) since feature extracting steps are not required. These automated models achieve low Mean Squared Errors in predicting the generator operational state (40.85 for Speed and 0.0018 for Load) and high accuracy in diagnosing rotor demagnetization failures (99.67%) by utilizing only raw current signals. We show how to create, deploy and run the collection of proposed models in a real-time setting using a laptop connected to a test rig via a data acquisition card. Based on a sampling rate of 5 kHz, predictions are stored in an efficient time series database and monitored using a dynamic visualization framework. We further discuss existing options for understanding the decision process behind the predictions made by the models.

scholarly journals Condition Monitoring of the Power Output of Wind Turbine Generators Using Wavelets

2010 ◽  
Vol 25 (3) ◽  
pp. 715-721 ◽  
Author(s):  
Simon Jonathan Watson ◽  
Beth J. Xiang ◽  
Wenxian Yang ◽  
Peter J. Tavner ◽  
Christopher J. Crabtree
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Power Output ◽  
Turbine Generators ◽  
Wind Turbine Generators
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