Radial Network Configuration Method Using Genetic Algorithm

Sawa, Toshiyuki; Kobayashi, Yasuhiro; Furukawa, Toshiyuki; Tamura, Shigeru

doi:10.1541/ieejeiss1987.121.11_1780

Cited by 6 publications

(10 citation statements)

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“…For tuning the population sizes in EAs using PG, PAO/CAO, conventional, and enhanced conventional operators, 28 experiments with sizes {1, 2, …, 10, 15, 20, …, 100} have been performed using 20 000 evaluations and 30 trials for different seeds of the random number generator. For EA using integer operators, only even population sizes are acceptable . Thus, 23 experiments with sizes {2, 4, …, 10, 14, 20, 24, …, 100} were performed.…”

Section: Resultsmentioning

confidence: 99%

“…In order to evaluate performance of the proposed method, five EAs have been implemented using: (i) PG operators, (ii) the original PAO/CAO operators, (iii) conventional operators, (iv) enhanced conventional operators, and (v) integer operators .…”

Section: Ea Implementationsmentioning

confidence: 99%

“…However, most them still demand high running time which is essentially affected by the modeling of the problem and employed operators. Besides, EA's conventional crossover/mutation operators cannot guarantee the production of only radial configurations which imposes extra computational burden for checking the radiality and possible repairs. In addition, the evaluation of the individuals that contain loops takes more time.…”

Section: Introductionmentioning

confidence: 99%

“…In order to evaluate effectiveness of PG operators, they are compared with the original PAO/CAO operators. In addition, performance of PG operators is compared with that of two sets of operators in a binary representation (conventional crossover/mutation operators and an enhanced version of them), called conventional and enhanced conventional operators hereafter, and a set of operators in an integer representation (conventional crossover and directed mutation operators) , called integer operators hereafter. Tests are performed on three networks with different sizes, number of feeders, and inside‐feeder tie lines where loss reduction is considered as the objective of optimization.…”

Section: Introductionmentioning

confidence: 99%

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Efficient reconfiguration of distribution networks using extended pruning‐grafting operators

RamezanJamaat

Akimoto

Aguirre

et al. 2014

IEEJ Transactions Elec Engng

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Network reconfiguration is a complicated, combinatorial, constrained optimization problem with many candidate switching options as well as structural and operational constraints. Introduction of evolutionary algorithms (EAs) to distribution network operation has opened many new opportunities. However, many applications of these methods suffer from high computational burden. In addition, conventional crossover/mutation operators cannot generally produce radial configurations. Performance of EAs is significantly affected by modeling of the problem and the employed operators. This paper employs a branch-based modeling of a distribution network and proposes two new EA operators that are an extension and redefinition of the preserve ancestor operator (PAO) and change ancestor operator (CAO). They are fast, exclusively produce radial configurations, and remove PAO/CAO operators' limitation. Hence, they can be utilized for a more efficient application of EAs to the network reconfiguration problem. Performance of the new operators is compared to the original PAO/CAO operators, two sets of operators in a binary representation (conventional crossover/mutation operators and an enhanced version of them), and a set of operators in an integer representation (conventional crossover and directed mutation operators). Simulations show the efficiency of the proposed method in terms of convergence speed, response time, and the quality of results.

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Section: Resultsmentioning

confidence: 99%

Section: Ea Implementationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient reconfiguration of distribution networks using extended pruning‐grafting operators

RamezanJamaat

Akimoto

Aguirre

et al. 2014

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…Since it is a greedy method, this approach suffers from the limitations found in such methods. In an attempt to solve this problem, a combination of this approach with GA was investigated in [33]. The authors observe that this combination usually produces unfeasible solutions.…”

Section: Discussionmentioning

confidence: 99%

Main Chain Representation for Evolutionary Algorithms Applied to Distribution System Reconfiguration

Delbem

Carvalho

Bretas

2005

IEEE Trans. Power Syst.

149

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Distribution system problems, such as planning, loss minimization, and energy restoration, usually involve network reconfiguration procedures. The determination of an optimal network configuration is, in general, a combinatorial optimization problem. Several Evolutionary Algorithms (EAs) have been proposed to deal with this complex problem. Encouraging results have been achieved by using such approaches. However, the running time may be very high or even prohibitive in applications of EAs to large-scale networks. This limitation may be critical for problems requiring online solutions.The performance obtained by EAs for network reconfiguration is drastically affected by the adopted computational tree representation. Inadequate representations may drastically reduce the algorithm performance. Thus, the employed representation for chromosome encoding and the corresponding operators are very important for the performance achieved. An efficient data structure for tree representation may significantly increase the performance of evolutionary-based approaches for network reconfiguration problems.The present paper proposes a tree encoding and two genetic operators to improve the EA performance for network reconfiguration problems. The corresponding EA approach was applied to reconfigure large-scale systems. The performance achieved suggests that the proposed methodology can provide an efficient alternative for reconfiguration problems.

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Development of outage restoration system using genetic algorithm

Matsumoto

Hase²,

Kinoshita³

2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077)

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Radial Network Configuration Method Using Genetic Algorithm

Cited by 6 publications

References 0 publications

Efficient reconfiguration of distribution networks using extended pruning‐grafting operators

Efficient reconfiguration of distribution networks using extended pruning‐grafting operators

Main Chain Representation for Evolutionary Algorithms Applied to Distribution System Reconfiguration

Development of outage restoration system using genetic algorithm

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