Alarms-related wind turbine fault detection based on kernel support vector machines

Wind power is playing an increasingly significant role in daily life. However, wind farms are usually far away from cities especially for offshore wind farms, which brought inconvenience for maintenance. Two conventional maintenance strategies, namely corrective maintenance and preventive maintenance, cannot provide condition-based maintenance to identify potential anomalies and predicts turbines' future operation trend. In this study, a model based data-driven condition monitoring method is proposed for fault detection of the wind turbines (WTs) with SCADA data acquired from an operational wind farm. Due to the nature of the alarm signals, the alarm data can be used as an intermedium to link the normal data and fault data. First, KPCA is employed to select principal components (PCs) to retain the dominant information from the original dataset to reduce the computation load for further modelling. Then the selected PCs are processed for normal-abnormal condition classification to extract those abnormal condition data that are classified further into false alarms and true alarms related to the faults. This two stage classification approach is implemented based on the KSVM algorithm. The results demonstrate that the two-stage fault detection method can identify the normal, alarm and fault conditions of the WTs accurately and effectively