Application of Improved GSA Algorithm and Time Series Method in Bad Data Identification in Power System

In the process of load forecasting under normal operation condition, there may be some of bad data in observing data of the power distributing systems, which will affect the reliability and accuracy of the processing result. Therefore, to detect and identify these bad data is particularly important. Fuzzy clustering analysis is a common method of bad data detection and recognition in power system, but its extreme sensitivity to the initial cluster center will lead to the inaccuracy of the classification results. In this paper, the power data is excavated on the basis of the hierarchical clustering algorithm, the gap statistical algorithm (GSA) and autoregressive integrated moving average model (ARMA), so as to complete bad data detection and recognition of the power system. In order to verify the correctness and effectiveness of the algorithm, the algorithm program is written in MATLAB, and the simulation analysis is carried out on the basis of massive power data in XIAMEN. The results show that the algorithm can effectively identify and reject bad data in power system, and therefore laying foundation for state estimation and medium and long term load forecasting of the power system.