Fault Diagnosis and Dynamic Threshold Early Warning for Wind Turbines Using DBSCAN‐MAD Anomaly Cleaning and GA‐Optimized LightGBM

ABSTRACT In order to resolve the contradiction between the rapid growth of wind turbines installed capacity and the lagging operation and maintenance technology, this article uses supervisory control and data acquisition (SCADA) system data to realize wind turbines fault diagnosis and early‐warning research. First, this article proposes a method for cleaning abnormal data in SCADA systems. It combines density‐based spatial clustering of applications with Noise (DBSCAN) and median absolute deviation (MAD) to estimate the normal power range, effectively removing outliers from the wind‐speed‐power curve and providing high‐quality health data samples for further research. Then, for wind turbines fault diagnosis, this article designs a lightweight gradient boosting machine (LightGBM) model based on a genetic algorithm (GA). This model achieves an overall fault diagnosis accuracy of 97%, effectively diagnosing different types of faults in wind turbines. Finally, this article proposes a multivariate state estimation technique (MSET) normal model based on the DBSCAN algorithm for fault warning in wind turbines. This article designs two threshold‐setting methods: fixed threshold and adaptive‐dynamic threshold. For gearbox faults, the fixed threshold method provides warnings 55 min in advance, while the adaptive dynamic threshold method advances the warning time to 68 min. For generator faults, the warning times are 134 and 148 min, respectively. Fault warning is conducted by calculating the residuals between predicted and actual values.