Distribution feeder reconfiguration with refined genetic algorithm

Lin, Whei-Min; Cheng, Fu-Sheng; Tsay, Ming-Tong

doi:10.1049/ip-gtd:20000715

Cited by 121 publications

(52 citation statements)

References 20 publications

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“…Most genetic algorithms applied in the optimization of power systems incorporate the unfeasibilities into the fitness through penalization or eliminate the unfeasible proposal altogether [28][29][30][31]. Usually unfeasible solutions can be eliminated in the optimization of power systems operation problems because they rarely appear.…”

Section: Traditional Genetic Algorithmmentioning

confidence: 99%

An Enhanced Genetic Algorithm to Solve the Static and Multistage Transmission Network Expansion Planning

Pareja

Rider

Lavorato

et al. 2012

Journal of Electrical and Computer Engineering

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An enhanced genetic algorithm (EGA) is applied to solve the long-term transmission expansion planning (LTTEP) problem. The following characteristics of the proposed EGA to solve the static and multistage LTTEP problem are presented, (1) generation of an initial population using fast, efficient heuristic algorithms, (2) better implementation of the local improvement phase and (3) efficient solution of linear programming problems (LPs). Critical comparative analysis is made between the proposed genetic algorithm and traditional genetic algorithms. Results using some known systems show that the proposed EGA presented higher efficiency in solving the static and multistage LTTEP problem, solving a smaller number of linear programming problems to find the optimal solutions and thus finding a better solution to the multistage LTTEP problem.

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Section: Traditional Genetic Algorithmmentioning

confidence: 99%

An Enhanced Genetic Algorithm to Solve the Static and Multistage Transmission Network Expansion Planning

Pareja

Rider

Lavorato

et al. 2012

Journal of Electrical and Computer Engineering

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“…This is because the some parts of the network are heavily loaded at certain time periods and lightly loaded at other time periods [5]. Therefore, the loads can be rescheduled by shifting them in such a way that the radial structure of the network can be altered efficiently to minimize the load unbalancing [6].…”

Section: Introductionmentioning

confidence: 99%

Implementation of ACO technique for load balancing through reconfiguration in electrical distribution system

Babu¹,

Shenoy²,

Ramya³

et al. 2014

2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD)

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Network reconfiguration in electrical distribution system is the process of changing the status of tie or sectionalizing switches, i.e., open or closed status to alter its topological structure. The proposed work attempts to reconfigure the system to balance loads while maintaining system radiality, using Ant Colony Optimization Technique. The burden of over loaded feeders is reduced by shifting partial loads to relatively lightly loaded feeders through reconfiguration of the system structure. The proposed technique provides various feasible solutions to balance loads on the feeders along with Graph Theory.

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“…The Tabu search attempted to determine a better solution in the manner of a greatest-descent algorithm, but it could not give any guarantee for the convergence property. Lin et al [5] presented a refined genetic algorithm (RGA) to reduce losses. Morton and Mareels presented a bruteforce solution for determining a minimal-loss radial configuration [6].…”

Section: Introductionmentioning

confidence: 99%

A hybrid evolutionary algorithm for distribution feeder reconfiguration

Niknam

Azad‐Farsani

2010

Sci. China Technol. Sci.

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This paper presents a new method to reduce the distribution system loss by feeder reconfiguration. This new method combines self-adaptive particle swarm optimization (SAPSO) with shuffled frog-leaping algorithm (SFLA) in an attempt to find the global optimal solutions for the distribution feeder reconfiguration (DFR). In PSO algorithm, appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort. Thus, a self-adaptive framework is proposed to improve the robustness of PSO. In SAPSO the learning factors of PSO coevolve with the particles. SFLA is combined with the SAPSO algorithm to improve its performance. The proposed algorithm is tested on two distribution test networks. The results of simulation show that the proposed algorithm is very powerful and guarantees to obtain the global optimization in minimum time.self-adaptive particle swarm optimization (SAPSO), discrete particle swarm optimization (DPSO), binary particle swarm optimization (BPSO), shuffled frog-leaping algorithm (SFLA), evolutionary algorithms (EA), distribution feeder reconfiguration (DFR) Citation:Niknam T, Azad Farsani E. A hybrid evolutionary algorithm for distribution feeder reconfiguration. Sci China Tech Sci, 2010, 53: 950−959,

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Distribution feeder reconfiguration with refined genetic algorithm

Cited by 121 publications

References 20 publications

An Enhanced Genetic Algorithm to Solve the Static and Multistage Transmission Network Expansion Planning

An Enhanced Genetic Algorithm to Solve the Static and Multistage Transmission Network Expansion Planning

Implementation of ACO technique for load balancing through reconfiguration in electrical distribution system

A hybrid evolutionary algorithm for distribution feeder reconfiguration

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