A New Binary Coding Particle Swarm Optimization for Feeder Reconfiguration

Wu, Wu-Chang; Tsai, Men‐Shen; Hsu, Fu-Yuan

doi:10.1109/isap.2007.4441672

Cited by 14 publications

(3 citation statements)

References 11 publications

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“…Batrinu et al [20] introduced particle swarm optimization method for distribution system optimal reconfiguration. Wu et al [21] proposed a binary coding particle swarm optimization for feeder reconfiguration. Su et al [22] introduced an ant colony search algorithm to solve the optimal network reconfiguration problem for power loss reduction.…”

Section: Introductionmentioning

confidence: 99%

Determination of Power Distribution Network Configuration Using Non-Revisiting Genetic Algorithm

Chun

Gao

2013

IEEE Trans. Power Syst.

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A non-revisiting genetic algorithm (NrGA) was used to determine distribution network configuration for loss reduction. By advocating binary space partitioning (BSP) to divide the search space and employing a novel BSP tree archive to store all the solutions that have been explored before, NrGA can quickly check for revisits by communicating with BSP tree archive when a new solution is generated by genetic algorithm (GA), and can mutate an alternative unvisited solution through a novel adaptive mutation mechanism that based on BSP tree while a revisit has occurred, which achieves no duplicates in the entire search. A method for getting independent loops of distribution network was realized using breadth-first search algorithm. Furthermore, the extended intermediate crossover mode, which requires no tuning parameter such as crossover rate and extends the crossover results, is employed for improving the performance of NrGA in solving distribution network configuration problem. The proposed approach has been successfully tested on three sample systems and three practical systems. Numerical studies have revealed its accuracy and efficient performance.

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Section: Introductionmentioning

confidence: 99%

Determination of Power Distribution Network Configuration Using Non-Revisiting Genetic Algorithm

Chun

Gao

2013

IEEE Trans. Power Syst.

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“…Another possibility is to take the infeasible configurations and changing them in order to obtain feasible configurations [35]. In [36] a dedicated shift operator is applied to modify the chromosome structure. The Prüfer number encoding is used in [37] to generate only radial configurations for GA applications.…”

Section: Discrete and Binary Pso Codingmentioning

confidence: 99%

Multi-Objective Distribution Network Reconfiguration Based on Pareto Front Ranking

et al. 2016

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Electrical distribution system reconfiguration is frequently addressed as a multi-objective problem, typically taking into account the system losses together with other objectives, among which reliability indicators are widely used. In the multi-objective context, Pareto front analysis enables the operator handling conflicting and even noncommensurable objectives without needing the use of additional hypotheses or weights. This paper provides advances on the application of Pareto front analysis to multi-objective distribution network reconfiguration. Starting from previous results in which genetic algorithms were effectively adopted to find the best-known Pareto front, a version of the multiobjective binary particle swarm optimization (MOBPSO) customized for distribution network reconfiguration has been developed by exploiting the internal ranking of the solutions (based on a multi-criteria decision making method in the selection of the local best) and the network topology. Furthermore, the Pareto front mismatch metric (already used by the authors to compare different methods for small networks for which the complete Pareto front can be calculated) has been generalized to be used with large systems for which only the best-known Pareto front is found. Applications to a test network and to a real urban distribution network are discussed, showing the consistent superiority of the customized MOBPSO version with respect to the application of genetic algorithms and of a more classical version of the particle swarm optimization method.

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“…Paper [35] proposes a reconfiguration methodology based on an Ant Colony Algorithm (ACA) that aims at achieving the minimum power loss and increment load balance factor of radial distribution networks with distributed generators. The obtainedresults have shown that lower system loss and better load balancing will be attained at a distribution system with distributed generation (DG) compared to a system without DG.In the past years, many artificial intelligent methods were proposed to solve the feeder reconfiguration problems: Particle Swarm Optimization methods [42]- [44], ant colony optimization methods (ACO) [29], [45], genetic algorithms (GA) [46]- [47], evolutionary algorithms (EA) [48]- [49], fuzzy algorithms [50] and so on.…”

Section: Introductionmentioning

confidence: 99%

Optimal Feeder Reconfiguration Optimization problem in Power Distribution Networks

Huseinagic

2016

scjournal

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Optimal feeder reconfiguration is a method used to determine optimal on/off statuses of tie and sectionalizing switches in order to reconfigure the network and improve certain objective goals. Mathematically, OFR is a mixed-integer nonlinear programsubjected to system constraints consisting of power flow equations, voltage limits, feeder capability limits and requirements for maintaining radial configuration of the network.In this paper, network reconfiguration problem is solved using branch exchange method. Solution involves a search for optimal on/off switch positionby transferring loads from one feeder to another, until no load transfer can further reduce the power losses, violations of voltage limits, and violations of branch capacity limits.Branch exchange method is applied on two feeder network, and results show that this method can be successfully used to decrease losses, improve voltage profile and resolve the overloading problem.

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A New Binary Coding Particle Swarm Optimization for Feeder Reconfiguration

Cited by 14 publications

References 11 publications

Determination of Power Distribution Network Configuration Using Non-Revisiting Genetic Algorithm

Determination of Power Distribution Network Configuration Using Non-Revisiting Genetic Algorithm

Multi-Objective Distribution Network Reconfiguration Based on Pareto Front Ranking

Optimal Feeder Reconfiguration Optimization problem in Power Distribution Networks

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