Efficient convex optimization for optimal PMU placement in large distribution grids
For power system operators, this work offers a computationally tractable approach to bound the optimal sensor placement problem in large-scale unbalanced distribution grids.
The paper addresses optimal PMU placement in large distribution grids to improve state estimation accuracy, proposing a convex optimization algorithm that provides a lower bound on the optimal solution. Tested on the IEEE 8500-node feeder, the method efficiently computes bounds, enabling gap analysis with heuristic solutions.
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.