Optimal PMU Placement for Power System State Estimation using Population-based Algorithms Incorporating Observability Requirements

This paper is devoted to the problem of the phasor measurement units (PMUs) placement for power system state estimation using optimality criteria proposed by the theory of optimal experimental design, such as A-, D-, M-, I-, G-optimality criteria. The high complexity of the task posed limits on the possibilities of solving it by exact mathematical methods only to small scale power systems. The paper studies the possibility to use population-based optimization algorithms (Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, and Ant Colony Optimization). To meet the state observability requirements, the repair procedure is incorporated in the population-based algorithms. This allows to overcome the drawbacks in the existing methods based on the assumption of a priory observability of the power system and to take into account the system contingencies such as the phasor failures, the PMU losses, and the branch outages. We demonstrate the effectiveness of the proposed method in terms of the PMU placement design's efficiency and computation efforts through the numerical simulations on a standard IEEE 118-bus system.