Efficient convex optimization for optimal PMU placement in large distribution grids

TL;DR

This paper introduces a convex optimization method to efficiently determine near-optimal sensor placement for improving state estimation in large, unbalanced distribution grids, addressing the NP-hard nature of the problem.

Contribution

The paper develops a convex optimization algorithm that provides a lower bound on optimal sensor placement, enabling efficient evaluation in large distribution networks.

Findings

The method effectively computes bounds on optimal sensor placement.

It demonstrates scalability on the IEEE 8500-node test feeder.

The approach improves state estimation accuracy in large grids.

Abstract

The small amount of measurements in distribution grids makes their monitoring more difficult. Topological observability may not be possible, and thus, pseudo-measurements are needed to perform state estimation, which is required to control elements such as distributed generation or transformers at distribution grids. Therefore, we consider the problem of optimal sensor placement to improve the state estimation accuracy in large-scale, 3-phase coupled, unbalanced distribution grids. This is an NP-hard optimization problem whose optimal solution is unpractical to obtain for large networks. Therefore, we develop a computationally efficient convex optimization algorithm to compute a lower bound on the possible value of the optimal solution, and thus check the gap between the bound and heuristic solutions. We test the method on a large test feeder, the standard IEEE 8500-node, to show the effectiveness of the approach.