Errors can happen – but it is important to recognize them. This also applies to the operation of wind power plants. Major damage and failures can be disastrous for the operators of such plants. The anomalies detected by monitoring systems often only indicate that a potential error is present or may occur.
In the future, monitoring systems could additionally include estimates of the underlying root cause. The ARCANA project deals with error diagnosis using deep learning. As a result, the AI system not only issues an alarm, but also specifies the causes that led to a malfunction.
The project is interesting to:
Wind power plant operators, maintenance companies, power plant manufacturers, insurance providers, modelers
Further development of anomaly detection in wind power plants towards error root cause analysis based on machine learning
Developing an algorithm that offers solutions for practical applications, enabling errors to be remedied more specifically, and error prevention measures to be planned ahead
Applying the method on an open dataset and on existing data