Multi-Objective PMU Allocation for Resilient Power System Monitoring

TL;DR

This paper proposes a multi-objective optimization approach using a modified Teaching-Learning-Based Optimization algorithm to optimally place PMUs in power systems, enhancing resilience, observability, and voltage stability.

Contribution

It introduces a novel multi-objective PMU placement model and a modified optimization algorithm, improving resilience and system monitoring in smart grids.

Findings

The method effectively balances PMU count, observability, and stability.

It outperforms existing methods in convergence speed and optimality.

Validated on IEEE test systems of various sizes.

Abstract

Phasor measurement units (PMUs) enable better system monitoring and security enhancement in smart grids. In order to enhance power system resilience against outages and blackouts caused by extreme weather events or man-made attacks, it remains a major challenge to determine the optimal number and location of PMUs. In this paper, a multi-objective resilient PMU placement (MORPP) problem is formulated, and solved by a modified Teaching-Learning-Based Optimization (MO-TLBO) algorithm. Three objectives are considered in the MORPP problem, minimizing the number of PMUs, maximizing the system observability, and minimizing the voltage stability index. The effectiveness of the proposed method is validated through testing on IEEE 14-bus, 30-bus, and 118-bus test systems. The advantage of the MO-TLBO-based MORPP is demonstrated through the comparison with other methods in the literature, in terms of iteration number, optimality and time of convergence.