PMU Placement Optimization for Smart Grid Obvervability and State Estimation

TL;DR

This paper develops optimized algorithms for PMU placement in power grids that ensure observability and improve state estimation accuracy, addressing limitations of previous methods by incorporating observability constraints.

Contribution

It introduces a novel PMU placement optimization framework that guarantees observability and enhances estimation accuracy, with efficient algorithms for large-scale networks.

Findings

Algorithms outperform existing methods in large IEEE networks.

Optimized placement improves state estimation accuracy.

Framework ensures observability constraints are met.

Abstract

In this paper, phasor measurement unit (PMU) placement for power grid state estimation under different degrees of observability is studied. Observability degree is the depth of the buses' reachability by the placed PMUs and thus constitutes an important characteristic for PMU placement. However, the sole observability as addressed in many works still does not guarantee a good estimate for the grid state. Some existing works also considered the PMU placement for minimizing the mean squared error or maximizing the mutual information between the measurement output and grid state. However, they ignore the observability requirements for computational tractability and thus potentially lead to artificial results such as acceptance of the estimate for an unobserved state component as its unconditional mean. In this work, the PMU placement optimization problem is considered by minimizing the mean squared error or maximizing the mutual information between the measurement output and grid state, under grid observability constraints. The provided solution is free from the mentioned fundamental drawbacks in the existing PMU placement designs. The problems are posed as binary nonlinear optimization problems, for which this paper develops efficient algorithms for computational solutions. The performance of the proposed algorithms is analyzed in detail through numerical examples on large-scale IEEE power networks.