Bad data identification in power system state estimation based on measurement compensation and linear residual calculation

Journal Article · Wed Feb 01 04:00:00 UTC 1989 · IEEE Trans. Power Syst.; (United States)

DOI:https://doi.org/10.1109/59.32457· OSTI ID:6052893

Slutsker, I W

This paper describes a new method of bad data identification. The method introduces several new concepts along with utilizing the advantages of the combinatorial optimization and hypothesis testing identification approaches. The method first sequentially eliminates suspected measurements until no gross errors remain in the measurement set and then performs the final identification by analyzing values of estimated errors of the suspected measurements. The vector of normalized residuals following each elimination is obtained without performing reestimation which results in high computational speed of the method. The measurement removal is efficiently performed through the application of special techniques, the measurement compensation and linear residual calculation, which are described in detail. The estimated errors of the suspected measurements are automatically available in the method upon completion of the elimination process. The method reliably identifies multiple interacting bad data. The results of the algorithm testing in a simulated energy management system (EMS) environment are reported.

• Research Organization: Control Data Corp., Minneapolis, MN (US)
• OSTI ID: 6052893
• Journal Information: IEEE Trans. Power Syst.; (United States), Journal Name: IEEE Trans. Power Syst.; (United States) Vol. 4:1; ISSN ITPSE
• Country of Publication: United States
• Language: English

Related Subjects

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29 ENERGY PLANNING, POLICY, AND ECONOMY
296000 -- Energy Planning & Policy-- Electric Power
ALGORITHMS
COMPUTER CALCULATIONS
DATA ANALYSIS
ENERGY MANAGEMENT SYSTEMS
ENERGY SYSTEMS
MATHEMATICAL LOGIC
MEASURING METHODS
OPTIMIZATION
POWER SYSTEMS
RELIABILITY