Based on the Fractal Technology of Bad Data Detection Method in Power System

Abstract

detection and identification of bad data is an important part of state estimation in power system. To solute the problem generates a variety of detection methods and means in academic and industrial circles, commonly used methods include objective function detection, weighted residual detection, measurement suddenly-change detection and the comprehensive application of above methods. In order to detection the bad data from large amounts of data over the multiple sliding windows, bad data detection algorithm is proposed based on fractal technology building monotonic search space. Firstly, it gives the data set on the piecewise fractal model, and then based on this model to design a detection algorithm. The algorithm can reduce detection processing time greatly. The subsection fractal model can accurately model on the data self similarity and compress data. Theoretical analysis and experimental results show that, the algorithm has higher precision and lower time / space complexity, more suitable for bad data detection.