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Hybrid algorithm of differential evolution and evolutionary programming for optimal reactive power flow

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4 Author(s)
C. Y. Chung ; Computational Intelligence Applications Research Laboratory (CIARLab), Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, People's Republic of China ; C. H. Liang ; K. P. Wong ; X. Z. Duan

Differential evolution (DE) is a promising evolutionary algorithm for solving the optimal reactive power flow (ORPF) problem, but it requires relatively large population size to avoid premature convergence, which will increase the computational time. On the other hand, evolutionary programming (EP) has been proved to have good global search ability. Exploiting this complementary feature, a hybrid algorithm of DE and EP, denoted as DEEP, is proposed in this study to reduce the required population size. The hybridisation is designed as a novel primary-auxiliary model to minimise the additional computational cost. The effectiveness of DEEP is verified by the serial simulations on the IEEE 14-, 30-, 57-bus system test cases and the parallel simulations on the IEEE 118-bus system test case.

Published in:

IET Generation, Transmission & Distribution  (Volume:4 ,  Issue: 1 )