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IJSTR >> Volume 5 - Issue 7, July 2016 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Optimizing Transmission Network Expansion Planning With The Mean Of Chaotic Differential Evolution Algorithm

[Full Text]

 

AUTHOR(S)

Ahmed R. Abdelaziz, Ahmed M.Fathy

 

KEYWORDS

Transmission Expansion Network Planning; Differential Evolution (DE); Lozi’s DE; Chaotic DE

 

ABSTRACT

This paper presents an application of Chaotic differential evolution optimization approach meta-heuristics in solving transmission network expansion planning (TNEP) using an AC model associated with reactive power planning (RPP). The reliability–redundancy of network analysis optimization problems implicate selection of components with multiple choices and redundancy levels that produce maximum benefits, can be subject to the cost, weight, and volume constraints is presented in this paper. Classical mathematical methods have failed in handling non-convexities and non-smoothness in optimization problems. As an alternative to the classical optimization approaches, the meta-heuristics have attracted lot of attention due to their ability to find an almost global optimal solution in reliability–redundancy optimization problems. Evolutionary algorithms (EAs) – paradigms of evolutionary computation field – are stochastic and robust meta-heuristics useful to solve reliability–redundancy optimization problems. EAs such as genetic algorithm, evolutionary programming, evolution strategies and differential evolution are being used to find global or near global optimal solution. The Differential Evolution Algorithm (DEA) population-based algorithm is an optimal algorithm with powerful global searching capability, but it is usually in low convergence speed and presents bad searching capability in the later evolution stage. A new Chaotic Differential Evolution algorithm (CDE) based on the cat map is recommended which combines DE and chaotic searching algorithm. Simulation results and comparisons show that the chaotic differential evolution algorithm using Cat map is competitive and stable in performance with other optimization approaches and other maps.

 

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