Enhanced anomaly detection and normal behaviour power curve modelling in wind farm SCADA data: A hybrid approach

Abstract

To achieve optimal performance and reduce the maintenance cost of wind turbines, anomaly detection and power curve modelling are crucial. The supervisory control and data acquisition (SCADA) system provides continuous and real-time data insights by collecting different wind-turbine operational parameters. This study introduces a novel strategy combining the strengths of Isolation Forest (iForest) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to identify and isolate anomalous data. The hybrid iForest-DBSCAN model processes enormous amounts of SCADA data to detect outliers and anomalies of wind turbines under different operating conditions. By utilizing normal data with minimum anomalies, normal behavious power curves (NBPC) were modelled using a robust Locally Estimated Scattered Smoothing (LOESS) technique. Robust power curves allow us to compare the performances of wind turbines and ensure an optimized function with minimum maintenance. Different datasets validated the proposed method with higher accuracy and fewer computational resource requirements than traditional methods. From the two wind farms, the iForest-DBSCAN model identified the datasets effectively while successfully generating NBPC with a 95% confidence interval. This study demonstrated the effectiveness of cutting-edge data-driven models and techniques for optimizing the efficiency and performance of wind farms.