Hybrid Physics-Based Adaptive Kalman Filter State Estimation Framework

Arturo S. Bretas, Newton G. Bretas, Julio A. D. Massignan and João B. A. London Junior
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Arturo S. Bretas: Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USA
Newton G. Bretas: Department of Electrical & Computer Engineering, University of Sao Paulo, Sao Carlos 13566-590, Brazil
Julio A. D. Massignan: Department of Electrical & Computer Engineering, University of Sao Paulo, Sao Carlos 13566-590, Brazil
João B. A. London Junior: Department of Electrical & Computer Engineering, University of Sao Paulo, Sao Carlos 13566-590, Brazil

Energies, 2021, vol. 14, issue 20, 1-17

Abstract: State-of-the art physics-model based dynamic state estimation generally relies on the assumption that the system’s transition matrix is always correct, the one that relates the states in two different time instants, which might not hold always on real-life applications. Further, while making such assumptions, state-of-the-art dynamic state estimation models become unable to discriminate among different types of anomalies, as measurement gross errors and sudden load changes, and thus automatically leads the state estimator framework to inaccuracy. Towards the solution of this important challenge, in this work, a hybrid adaptive dynamic state estimator framework is presented. Based on the Kalman Filter formulation, measurement innovation analytical-based tests are presented and integrated into the state estimator framework. Gross measurement errors and sudden load changes are automatically detected, identified, and corrected, providing continuous updating of the state estimator. Towards such, the asymmetry index applied to the measurement innovation is introduced, as an anomaly discrimination method, which assesses the physics-model-based dynamic state estimation process in different piece-wise stationary levels. Comparative tests with the state-of-the-art are presented, considering the IEEE 14, IEEE 30, and IEEE 118 test systems. Easy-to-implement-model, without hard-to-design parameters, build-on the classical Kalman Filter solution, highlights potential aspects towards real-life applications.

Keywords: measurement innovation; asymmetry index; Kalman Filter; anomaly detection; measurement gross errors; load dynamics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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