Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern

Abstract

Condition monitoring (CM) of wind turbines (WTs) is commonly accepted as an effective way to increase the availability and reduce the operation and maintenance (O&M) costs of wind farms. CM methods based on data from Supervisory Control and Data Acquisition (SCADA) system have garnered a great deal of interest due to their accessibility and low cost. Existing studies based on SCADA data provide whether the WT is normal or abnormal, and further the fault locations if an anomaly occurs. Nevertheless, few studies have attempted to identify the underlying causes of the anomaly. In this paper, a novel WTCM method utilizing a self-attention-based mechanism embedded with a multivariable query pattern is proposed for the anomaly detection and underlying causes identification. Firstly, an anomaly detection model composed of multiple cascaded encoders and decoders with a multi-head self-attention mechanism is proposed to extract cross-variable correlations. Furthermore, the multivariable query pattern is designed to evaluate the influences of different features on the target, which is essential for fault identification. Finally, the relative anomaly index (RAI) is proposed for each feature to quantify the impact of each feature on the anomaly. RAIs are adopted to analyze the underlying causes of anomalies. Experimental results confirmed the effectiveness of the proposed method for the WT fault early detection and identification of anomaly causes.