The Elbow Criterion Based on GSA for Bad Data Identification of Power System

Abstract

Based on bad data detection using GSA (Gap Statistic Algorithm) data mining method in powersystem, this paper propose the elbow criterion to estimate optimal clustering number. The criterion analyzes the relation between the degree of clustering dispersion and clustering number k of the data set firstly, then calculates the elbow angle at k and obtain the optimal clustering number based on the least elbow angle. Combined the criterion with GSA, bad data detection could be implemented efficiently. Computer results show that the integrated method not only can avoid residual pollution and residual submersion which would appear using traditional state estimate detection, but also is more accurate and rapid than GSA method. In the case of huge system and large amount of data, this method is a rapid and efficient algorithm, and has potential of good application.