A method for cleaning wind power anomaly data by combining image processing with community detection algorithms

Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data. Consequently, a method for cleaning wind power anomaly data by combining image processing with community detection algorithms (CWPAD-IPCDA) is proposed. To precisely identify and initially clean anomalous data, wind power curve (WPC) images are converted into graph structures, which employ the Louvain community recognition algorithm and graph- theoretic methods for community detection and segmentation. Furthermore, the mathematical morphology operation (MMO) determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning. The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines (WTs) in two wind farms in northwest China to validate its feasibility. A comparison was conducted using density-based spatial clustering of applications with noise (DBSCAN) algorithm, an improved isolation forest algorithm, and an image-based (IB) algorithm. The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms, achieving an approximately 7.23% higher average data cleaning rate. The mean value of the sum of the squared errors (SSE) of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms. Moreover, the mean of overall accuracy, as measured by the F1-score, exceeds that of the other methods by approximately 10.49%; this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.