Optimal Placement of PMUs Using Adaptive Genetic Algorithm Considering Measurement Redundancy

Abstract

With the extensive use of PMUs in power system, how to implement the optimal PMU placement for complete system observability is becoming more and more significant. Against simple genetic algorithm, easy to fall into local minima, this paper proposed a simple and effective adaptive genetic algorithm, aimed at determining the optimal placement of PMUs. The proposed algorithm provides adaptive crossover and mutation probability in iterations and has better convergence and global search ability than simple genetic algorithm, confirmed in many practical applications. Generally speaking, minimum PMU placement problem has multiple solutions, and this paper proposed an index of observability redundancy to rank these multiple solutions. Moreover, several observability rules were also put forward and proved with great help in obtaining the optimal solution. Simulations using MATLAB are conducted on IEEE 7 bus, 14 bus, 24 bus, 30 bus, 57 bus, 118 bus and New England 39 bus test systems in order to verify the validity of this adaptive method. Results compared with other existing methods show that the proposed adaptive method is simple to realize and obtains the optimal solution with high redundancy in very few generations, which is much more effective than the results by major existing algorithms and shows a good reference value.