Development of a baseline as a novel strategy for a condition monitoring system for the assessment of wind turbine generators

According to the failure of wind turbines in operation, the failure cause and phenomenon of wind turbines is analyzed, combined with the reliability of wind turbine subsystems, measures aiming at cooperation parts and purchased parts are proposed, the reliability of the whole wind turbines is improved in a certain extent. At the same time, condition monitoring system can carry through the early detecting and diagnosing to potential component failure maintain. Besides, automatic lubrication system can realize accurate and timeliness lubrication, also can reduce maintenance workload, preserve correct lubrication and smooth running of all parts.

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Smart Wind Turbine: Artificial Intelligence based Condition Monitoring System

Proceedings of the 8th International Conference on Smart Cities and Green ICT Systems ◽

10.5220/0007767701940198 ◽

2019 ◽

Author(s):

Afshin Tafazzoli ◽

Alvaro Mayo

Keyword(s):

Artificial Intelligence ◽

Wind Turbine ◽

Monitoring System ◽

Condition Monitoring ◽

Condition Monitoring System

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

Energies ◽

10.3390/en13184817 ◽

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.

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