An Anomaly Detection Approach Based on Machine Learning and SCADA Data for Condition Monitoring of Wind Turbines

This paper presents an anomaly detection approach using machine learning to achieve condition monitoring for wind turbines. The approach applies the information in supervisory control and data acquisition systems as data input. First, machine learning is used to estimate the temperature signals of the gearbox component. Then the approach analyzes the deviations between the estimated values and the measurements of the signals. Finally, the information of alarm logs is integrated with the previous analysis to determine the operation states of wind turbines. The proposed approach has been tested with the data experience of a 2MW wind turbine in Sweden. The result demonstrates that the approach can detect possible anomalies before the failure occurrence. It also certifies that the approach can remind operators of the possible changes inside wind turbines even when the alarm logs do not report any alarms.