Optimal PMU Placement for Power System Dynamic State Estimation by Using Empirical Observability Gramian

TL;DR

This paper introduces an optimization-based method for optimal PMU placement in power systems using the empirical observability Gramian, enhancing dynamic state estimation accuracy and robustness.

Contribution

It formulates an optimization approach maximizing the Gramian's determinant for optimal PMU placement, validated on standard power system models.

Findings

Optimal placements improve observability and estimation accuracy.

Method is robust to load fluctuations and contingencies.

Significant reduction in estimation errors compared to random placement.

Abstract

In this paper the empirical observability Gramian calculated around the operating region of a power system is used to quantify the degree of observability of the system states under specific phasor measurement unit (PMU) placement. An optimal PMU placement method for power system dynamic state estimation is further formulated as an optimization problem which maximizes the determinant of the empirical observability Gramian and is efficiently solved by the NOMAD solver, which implements the Mesh Adaptive Direct Search (MADS) algorithm. The implementation, validation, and also the robustness to load fluctuations and contingencies of the proposed method are carefully discussed. The proposed method is tested on WSCC 3-machine 9-bus system and NPCC 48-machine 140-bus system by performing dynamic state estimation with square-root unscented Kalman filter. The simulation results show that the determined optimal PMU placements by the proposed method can guarantee good observability of the system states, which further leads to smaller estimation errors and larger number of convergent states for dynamic state estimation compared with random PMU placements. Under optimal PMU placements an obvious observability transition can be observed. The proposed method is also validated to be very robust to both load fluctuations and contingencies.