Precision Enhancement of Distribution System State Estimation via Tri-Objective Micro Phasor Measurement Unit Deployment

TL;DR

This paper proposes a tri-objective optimization method for placing micro phasor measurement units in power distribution systems to improve state estimation accuracy, robustness, and cost-effectiveness, considering system failures and uncertainties.

Contribution

It introduces a novel placement approach using a customized NSGA-II algorithm that balances multiple objectives and accounts for system contingencies and zero injection nodes.

Findings

Optimal placement can reduce the number of {b5}-PMUs needed by 30-40%.

Additional {b5}-PMUs beyond a certain point yield minimal benefits.

The method maintains system observability and accuracy even with partial instrumentation.

Abstract

A tri-objective optimal Micro Phasor Measurement Units ({\mu}-PMUs) Placement method is presented, with a focus on minimizing the following three parameters: i) the total number of {\mu}-PMU channels, (ii) the maximum state estimation uncertainty, and (iii) the sensitivity of state estimation to line parameter tolerances. The suggested formulation takes single-line and {\mu}-PMU failures into consideration while guaranteeing the complete observability of the system in the presence and absence of contingencies. It also takes into account the impact of zero injection nodes and the quantity of {\mu}-PMU channels carried out at every node. The suggested placement issue is addressed using a customized version of the nondominated sorting genetic algorithm II (NSGA-II). According to the results achieved utilizing three test systems of varying sizes, {\mu}-PMU channels beyond predetermined thresholds only result in higher costs and negligible further decreases in state estimation uncertainty and sensitivity to line parameter tolerances. Additionally, we may omit to instrument between 30 and 40% of buses if {\mu}-PMUs with only two three-phase channels are utilized, with only a modest negative effect on state estimate performance even in the event of contingencies.