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.
Highlights
Electric power systems are critical infrastructures that rely on continuous and detailed monitoring of their assets, encompassed by power system state estimation (PSSE)
The exploration of the asymmetry index as an anomaly discrimination method, assessing the dynamic state estimation (DSE) process in different piecewise stationary levels, which can be used in any family of Kalman Filters-like dynamic state estimators framework
This paper presented a family of Kalman filter modeling for the power system dynamic state estimation problem
Summary
Electric power systems are critical infrastructures that rely on continuous and detailed monitoring of their assets, encompassed by power system state estimation (PSSE). Robustness is often obtained at the cost of improving the estimator variance, computational burden, or model complexity Such approaches fail to discriminate measurement gross errors from changes in the stationary load level, often treating them in the same theoretical perspective, or by modifying the residual-based tests. Amidst such a promising challenge, this work explores a family of Kalman Filters formulation for the DSE problem. The exploration of the asymmetry index as an anomaly discrimination method, assessing the DSE process in different piecewise stationary levels, which can be used in any family of Kalman Filters-like dynamic state estimators framework.
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