Wrong Active Power Data Identification and Correction for WAMS Based on Pattern Recognition
Chulin Wan, Haoyong Chen, Manlan Guo · Power System Technology · 2017
At present, error warning problems are produced in wide area measurement system(WAMS) because of wrong data injection. To overcome problem of time delay and high non-response rates when using common methods to process wrong data, a rapid identification and recovery method based on pattern recognition concept is put forward in this paper. According to WAMS data characteristics, standard feature vectors are set in advance. Then all WAMS measured data are changed to unique feature vectors in the same way and matched with the standard feature vectors, so that wrong data can be easily and quickly identified. Finally, three cases are designed to show identification and recovery results of three kinds of data including correct data in transient state, wrong data in steady state and a large amount of real data. Result demonstrates that, without repeated iterations and injection of power grid parameters, pattern recognition has strong advantages on both accuracy and computational time to provide data support for power grid state monitoring and analysis.