A New Model for Multi-Objective PMU Placement Considering Actual Worth of Uncertainties Using Cellular Learning Automata

Phasor measurement units (PMUS) are crucial elements of wide-area state estimation system in Smart Grids, as they maintain a high quality observability on electrical quantities of power system. This paper proposes a new approach for multi-objective PMU placement considering actual worth of contingency conditions. Moreover, a new fitness function is introduced to simultaneously find the minimum number of PMUs as well as to maximize the measurement redundancies. In addition, a cellular learning automata based algoritm is employed for optimization process. The developed method is applied to IEEE test systems and obtained results are reported in several scenarios. Detailed numerical results and comparisons presented in the paper show that the proposed approach could noticeably improve the quality of problem solutions under uncertainties and can be used as an effective tool for multi-objective PMU placement within an actual large-scale transmission network.