A semi-supervised anomaly detection method for wind farm power data preprocessing

Wind farm power data is an essential data source for characteristics analysis of wind farms, and its precision highly influences the operation and control of wind power. Existing methods for abnormal wind power data detection are mainly based on supervised or unsupervised algorithms, which may suffer from huge effort consumption on artificial label-setting or improvable detection precision respectively. In this paper, a semi-supervised anomaly detection method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed. Supervision from a limited number of labelled data is utilized to guide the anomaly detection process. Simulation based on realistic wind farm power data from China is performed to verify the method validity. The simulation results illustrate that the proposed method can obtain a satisfactory performance on avoiding false dismissals and false alarms among irregular-shaped wind power clusters, as well as an improved distinguishing ability around the confusing boundaries between normal and abnormal data.