A multi-functional dynamic state estimator for error validation: measurement and parameter errors and sudden load changes

Abstract

We propose a new and efficient algorithm to detect, identify, and correct measurement errors and branch parameter errors of power systems. A dynamic state estimation algorithm is used based on the Kalman filter theory. The proposed algorithm also successfully detects and identifies sudden load changes in power systems. The method uses three normalized vectors to process errors at each sampling time: normalized measurement residual, normalized Lagrange multiplier, and normalized innovation vector. An IEEE 14-bus test system was used to verify and demonstrate the effectiveness of the proposed method. Numerical results are presented and discussed to show the accuracy of the method.

Keywords

Dynamic state estimation / Kalman filter / Measurement errors / Branch parameter errors / Sudden load changes

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2016 Zhejiang University and Springer-Verlag Berlin Heidelberg