Optimal Configuration of PMU in Power System

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In order to use the least number of phasor measurement unit (PMU) to guarantee the power system completely observable, an optimal PMU configuration method in power system was put forward. The pre-configuration of PMU was done considering the actual situation of power grid. The genetic algorithm (GA) was used for PMU configuration. Modify the formulae of crossover probability and mutation probability in traditional genetic algorithm to overcome the evolutionary stagnation when the maximum fitness value and the average fitness value in group were equal. The improved adaptive genetic algorithm (IAGA) was obtained. In order to eliminate the premature convergence of GA resulted from the chance and randomness of the crossover operation and mutation operation, the preventing premature operation was introduced. This method combined the IAGA and the preventing premature operation. It has good global astringency, and it can ensure the network complete observability with the minimum number of PMU.

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659-665

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February 2015

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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[1] Phadke A G, Thorp J S, Karimi K J. State estimation with phasor measurements[J]. IEEE Power Engineering Review, 1986, 6(2): 233-241.

DOI: 10.1109/mper.1986.5528179

Google Scholar

[2] Min Sun, Junjie Xiong, Jie Zhang, et al. Optimal PMU disposition in power system based on maximal measurement tree[J]. Electric Power Automation Equipment, 2008, 28(8): 73-76. (In Chinese).

Google Scholar

[3] Hua Lan, Wangjing Wan, Yunran Wang, et al. An optimal PMUs placement in power systems based on the simulated annealing and genetic algorithm[J]. Power System Technology, 2007, 31(2): 114-117. (In Chinese).

Google Scholar

[4] Zhiming Sha, Yuqian Hao, Yushan Hao, et al. A new algorithm for PMU placement optimization in power system[J]. Relay, 2005, 33(7): 31-36. (In Chinese).

Google Scholar

[5] Tiantian Cai, Qian Ai. Research on optimal PMU placement in power systems [J]. Power System Technology, 2006, 30(13): 32-37. (In Chinese).

Google Scholar

[6] Zuopeng Zhang, Gang Chen, Maojin Bai. PMU optimal placement based on dynamic programming[J]. Power System Technology, 2007, 31(1): 52-56. (In Chinese).

Google Scholar

[7] Yuqian Duan, Jiali He. Genetic algorithm and its modification [J]. Proceedings of the EPSA, 1998, 10(1): 39-52. (In Chinese).

Google Scholar

[8] Srinivas M, Patnaik L M. Adaptive probabilities of crossover and mutation in genetic algorithms [J]. IEEE Transactions on Systems Man and Cybernetics, 1994, 24(4): 656-667.

DOI: 10.1109/21.286385

Google Scholar

[9] Ziwu Ren, Ye San. Improved adaptive genetic algorithm and its application research in parameter identification[J]. Journal of System Simulation, 2006, 18(1): 41-66. (In Chinese).

Google Scholar

[10] Xiaoping Wang, Liming Cao. Genetic Algorithm-theory, application and software[M]. Xi'an: Xi'an Jiao Tong University press, 2002. (In Chinese).

Google Scholar