Improved GSA-GA Algorithm Based Bad Data Detection, Identification and Correction for Power Grid

Abstract:

This paper presents an improved GSA-GA algorithm to achieve bad-data detection, identification and correction in power grid. The algorithm combines BP neural network, K-means clustering algorithm, gap statistical algorithm (GSA) and genetic algorithm together. BP neural network preprocesses the data, K-means algorithm clusters the preprocessed data and GSA algorithm determines the optimal clustering number and identifies the presence of bad data. After identifying the bad data, GA-BP algorithm is used to correct the identified data. This paper takes simulation tests to verify the proposed algorithms correctness and effectiveness based on actual grid data considering multiple types of existed bad data.

• Periodical: Advanced Materials Research (Volumes 860-863)
• Pages: 2470-2473
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.860-863.2470
• Online since: December 2013
• Authors: Hai Feng Liang*, Ding Hui Shen, Xiao Lei Yu, Jing Zhang, Cheng Shan Wang
• Keywords: Bad Data Detection, BP Neural Network (BPNN), GSA-Ga, State Estimation

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