In this paper, optimal placement of Phasor Measurement Unit (PMU) using Global Positioning System (GPS) is discussed. Ant Colony Optimization (ACO), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used for this problem. Pheromone evaporation coefficient and the probability of moving from state x to state y by ant are introduced into the ACO. The modified algorithm overcomes the ACO in obtaining global optimal solution and convergence speed, when applied to optimizing the PMU placement problem. We also compare this simulink with SA, PSO and GA that to find capability of ACO in the search of optimal solution. The fitness function includes observability, redundancy and number of PMU. Logarithmic Least Square Method (LLSM) is used to calculate the weights of fitness function. The suggested optimization method is applied in 30-bus IEEE system and the simulation results show modified ACO find results better than PSO and SA, but same result with GA.

Keywords: Evolutionary Algorithms , Global Positioning System , Optimal Placement , Phasor Measurement Unit

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