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

C. Y. Chung, C. H. Liang, K. P. Wong, X. Z. Duan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

39 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article numberIGTDAW000004000001000084000001
Pages (from-to)84-93
Number of pages10
JournalIET Generation, Transmission and Distribution
Volume4
Issue number1
DOIs
Publication statusPublished - Jan 2010

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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