Adaptive Multi-Step Prediction based EKF to Power System Dynamic State Estimation

TL;DR

This paper introduces an adaptive multi-step prediction method to enhance the extended Kalman filter's accuracy and efficiency in power system dynamic state estimation by adjusting prediction steps based on non-linearity levels.

Contribution

It proposes a novel adaptive multi-step prediction approach that dynamically adjusts prediction steps in EKF based on non-linearity indexes for improved power system state estimation.

Findings

Achieves a good balance between estimation accuracy and computational efficiency.

Demonstrates effectiveness using a two-area four-machine system simulation.

Monte-Carlo results validate the approach's robustness.

Abstract

Power system dynamic state estimation is essential to monitoring and controlling power system stability. Kalman filtering approaches are predominant in estimation of synchronous machine dynamic states (i.e. rotor angle and rotor speed). This paper proposes an adaptive multi-step prediction (AMSP) approach to improve the extended Kalman filter s (EKF) performance in estimating the dynamic states of a synchronous machine. The proposed approach consists of three major steps. First, two indexes are defined to quantify the non-linearity levels of the state transition function and measurement function, respectively. Second, based on the non-linearity indexes, a multi prediction factor (Mp) is defined to determine the number of prediction steps. And finally, to mitigate the non-linearity impact on dynamic state estimation (DSE) accuracy, the prediction step repeats a few time based on Mp before performing the correction step. The two-area four-machine system is used to evaluate the effectiveness of the proposed AMSP approach. It is shown through the Monte-Carlo method that a good trade-off between estimation accuracy and computational time can be achieved effectively through the proposed AMSP approach.