Distribution System Reconfiguration for Loss Reduction Based on Ant Colony Behavior

Pereira, Flávia Thomaz Veréchia; Vittori, Karla; Costa, Geraldo Roberto Martins da

doi:10.1109/tdcla.2006.311448

Cited by 6 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Crowding distance is a way to calculate the diversity of solutions within the same rank. Equations (11) and (12) are used to the calculation of crowding distance between solutions [21].…”

Section: The Non-dominated Sorting Genetic Algorithm IImentioning

confidence: 99%

“…fuzzy logic methods [2,3], tabu search [4,5], genetic algorithm(GA)/evolutional programming (EP) [6-8], particle swarm optimization (PSO) [9,10], ant colony optimization (ACO) [11][12][13][14], and so on. System reconfiguration can be used for system loss reduction or voltage profile improvement during normal operations.…”

mentioning

confidence: 99%

“…The results of this study indicated that the proposed non-dominated sorting evolutionary programming was capable of identifying better solutions than that of non-dominated sorting genetic algorithm for distribution system reconfiguration problems. fuzzy logic methods [2,3], tabu search [4,5], genetic algorithm(GA)/evolutional programming (EP) [6-8], particle swarm optimization (PSO) [9,10], ant colony optimization (ACO) [11][12][13][14], and so on. The objectives considered by the aforementioned research include: system loss [1,4,[6][7][8]10,[13][14][15]17], load balancing [4,5,8,16,18], number of switch operations [2,3,5,7,8], and so on.…”

mentioning

confidence: 99%

See 2 more Smart Citations

A non-dominated sorting evolutionary programming algorithm for multi-objectives power distribution system feeder reconfiguration problems

Hsu

Tsai

2011

Int. Trans. Electr. Energ. Syst.

View full text Add to dashboard Cite

SUMMARY Distribution system reconfiguration is performed during normal or emergency operations by changing the status of switches. System reconfiguration can be used for system loss reduction or voltage profile improvement during normal operations. For emergency operations, de‐energized loads can be transferred to adjacent energized feeders through system reconfiguration. Several objectives, for example, system real power loss reduction and transformer load balancing improvement, are considered simultaneously during system reconfiguration. In order to consider the objectives simultaneously, weighted sum method was commonly used. Only one solution may be obtained when weighted sum method was applied. However, several compromise solutions were preferred for the distribution system operators. Multi‐objective approach was one of the methods that can be used to provide multiple compromise solutions. In this paper, the fitness function in non‐dominated sorting genetic algorithm and the reproduction procedure in evolutionary programming were integrated. The proposed non‐dominated sorting evolutionary programming was applied to the multi‐objective problems in distribution systems in order to provide system operators with different compromise solutions. The results of this study indicated that the proposed non‐dominated sorting evolutionary programming was capable of identifying better solutions than that of non‐dominated sorting genetic algorithm for distribution system reconfiguration problems. Copyright © 2011 John Wiley & Sons, Ltd.

show abstract

“…Crowding distance is a way to calculate the diversity of solutions within the same rank. Equations (11) and (12) are used to the calculation of crowding distance between solutions [21].…”

Section: The Non-dominated Sorting Genetic Algorithm IImentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

A non-dominated sorting evolutionary programming algorithm for multi-objectives power distribution system feeder reconfiguration problems

Hsu

Tsai

2011

Int. Trans. Electr. Energ. Syst.

View full text Add to dashboard Cite

show abstract

“…is the normalized cost of the th objective associated with a solution and can be obtained as (17) where is the objective function of the th objective. is the normalization function for the th objective.…”

Section: B Integration Of Multi-objective Optimization Problemsmentioning

confidence: 99%

“…Therefore, heuristic methods such as expert system [1], fuzzy logic [2], [3], Tabu search [4], [5], genetic algorithm (GA)/evolutional programming (EP) [6]- [11], particle swarm optimization (PSO) [12], [13], ant colony optimization (ACO) [14]- [17], etc. were proposed in the past.…”

Section: Introductionmentioning

confidence: 99%

Application of Grey Correlation Analysis in Evolutionary Programming for Distribution System Feeder Reconfiguration

Tsai

Hsu²

2010

IEEE Trans. Power Syst.

118

View full text Add to dashboard Cite

Feeder reconfiguration is a common technique that is used by distribution system operators during normal or emergency operational planning. By changing the status of switches on the distribution systems, the feeders can be reconfigured. During a feeder reconfiguration, more than one objective is considered by the distribution system operators. Due to the complexity of the reconfiguration problems, the system operators are looking for assistance from computer program that can provide adequate switching plans to reconfigure the feeders such that the desired goal can be achieved. Thus, the feeder reconfiguration is a type of discrete multi-objective optimization problems. Evolutionary programming (EP) technique is a method that can be applied to identify an optimal switching plan for feeder reconfiguration. A fitness function is required in EP for chromosome selection during reproduction process. The fitness function needs to integrate the objectives to provide a measure for each chromosome. Normalizing the objectives is a typical method for multi-objective optimizations such that these objectives are comparable. In this paper, Gray CoRrelation Analysis (GCRA) method is proposed. The proposed method is used to integrate the objectives and provide a relative measure to a particular switching plan associated with a chromosome without any prior knowledge of the system under reconfiguration. Two different distribution systems are used in this paper to demonstrate how the proposed GCRA is applied during the selection process of EP. Several simulations show that the EP can identify the solution more accurately when GCRA is applied than other methods.

show abstract