Method for Detecting Anomaly Data of WAMS System Based on GA-iForest
Hongwen Yan, Jiawei Li, Jian Hua Zuo, Jihong Tang · IOP Conference Series Earth and Environmental Science · 2020
Abstract Wide area measurement system problems such as anomalies, data missing, and real-time response deeply affect the operation and maintenance of the power grid. Considering the accuracy requirements of the system for anomaly detection, the detection accuracy and stability of traditional isolation forest are poor, so this paper proposes a new data anomaly detection method named GA-iForest. This method uses genetic algorithms to select isolated trees with high accuracy and obvious differences to optimize the structure of isolated forests. The new data anomaly detection method GA-iForest improves the accuracy and stability of detection, and mean while maintaining the high efficiency. The experiment uses standard simulation data sets and data from one province’s wide-area measurement system for experimental simulation. The result shows that the GA-iForest method has significantly improved accuracy and stability compared with the traditional isolation forest, LOF, and K-means methods.