MATEC Web Conf.
Volume 119, 2017The Fifth International Multi-Conference on Engineering and Technology Innovation 2016 (IMETI 2016)
|Number of page(s)||11|
|Published online||04 August 2017|
A backtracking evolutionary algorithm for power systems
1 Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan
2 Department of Electrical Engineering, WuFeng University, Chiayi County 62153, Taiwan
a Corresponding author : email@example.com
This paper presents a backtracking variable scaling hybrid differential evolution, called backtracking VSHDE, for solving the optimal network reconfiguration problems for power loss reduction in distribution systems. The concepts of the backtracking, variable scaling factor, migrating, accelerated, and boundary control mechanism are embedded in the original differential evolution (DE) to form the backtracking VSHDE. The concepts of the backtracking and boundary control mechanism can increase the population diversity. And, according to the convergence property of the population, the scaling factor is adjusted based on the 1/5 success rule of the evolution strategies (ESs). A larger population size must be used in the evolutionary algorithms (EAs) to maintain the population diversity. To overcome this drawback, two operations, acceleration operation and migrating operation, are embedded into the proposed method. The feeder reconfiguration of distribution systems is modelled as an optimization problem which aims at achieving the minimum loss subject to voltage and current constraints. So, the proper system topology that reduces the power loss according to a load pattern is an important issue. Mathematically, the network reconfiguration system is a nonlinear programming problem with integer variables. One three-feeder network reconfiguration system from the literature is researched by the proposed backtracking VSHDE method and simulated annealing (SA). Numerical results show that the perfrmance of the proposed method outperformed the SA method.
© The Authors, published by EDP Sciences, 2017
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