Stability Analysis of Optimized PMU Placement using Hybrid and Individual TLBO-PSO Techniques

In power system to optimized PMUs is a critical task to ensure maximum network observability while minimizing installation costs. This study presents a comparative analysis of three optimization techniques: Teaching-Learning-Based Optimization (TLBO), Particle Swarm Optimization (PSO), and a hybrid TLBO-PSO approach, focusing on their efficiency in determining the best PMU placements. Individual methods, such as TLBO and PSO, are often limited by longer computation times and the requirement for a higher number of PMUs to achieve full observability. In contrast, the hybrid TLBO-PSO method demonstrates significant improvements, consistently delivering solutions with fewer PMUs, faster computation times, and higher placement accuracy. By evaluating performance of these techniques on IEEE 14bus, 30bus and 57 bus systems through simulations conducted over 100 iterations for each method in every test case. The results highlight the hybrid approach's superior efficiency compared to individual methods. Furthermore, comparisons with prior research confirm that the hybrid TLBO-PSO approach is a robust and reliable solution for minimizing PMU installations while ensuring complete system observability.