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A New Adaptive Genetic Algorithm and Its Application in Optimizing Phasor Measurement Units Placement in Electric Power System

Sha Zhiming Hao Yuqian Hao Yushan Yang Yihan (North China Electric Power University Baoding 071003 China)  
This paper presents a new adaptive genetic algorithm to optimize the PhasorMeasurement Units (PMU) placement problem. The evolution attenuator factor is introduced into the genetic algorithm, which enables the new adaptive genetic algorithm to adjust the possibilities ofcrossover and mutation adaptively according to both the individual fitness and evolution generations. In the traditional adaptive algorithm, the high fitness solutions?possibilities of crossover and mutation are near zero in the early generations and the algorithm may get stuck at the near-optimal solution. The new adaptive algorithm keeps the crossover and mutation pressure to the high fitness solutions during the early and mid-term generations, which would introduce new solutions into the populations quicker and help the algorithm converge to global optimum. In the late generations, the attenuator factor decreases the possibilities of crossover and mutation sharply, thus the global optimal solutions are protected from disruption. The new adaptive genetic algorithm is applied to PMU placement optimization and fulfills the requirement of minimizing the number of PMUs in the system while keeping the all nodes voltage phasor observable. A graph-theoretic procedure based on depth first search is adopted to analyze thesystem observability. Illustrative results on the IEEE 14-bus system and a provincial 46-bus system are provided.
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