A new intelligent optimization technique for distribution systems reconfiguration

Mekhamer, S. F.; Abdelaziz, Almoataz Y.; Mohammed, Fareeduddin; Badr, M.A.

doi:10.1109/mepcon.2008.4562296

Cited by 20 publications

(15 citation statements)

References 12 publications

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“…In [11], a method to maximize the benefit to network operators and owners of distributed generation in a deregulated electricity market hybrid algorithm was presented. As well as simultaneously optimizing the benefits to the distribution company, and to the owner of the DG, their method also considered the uncertainty of demand and energy prices [12].…”

Section: Introductionmentioning

confidence: 99%

Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms

Ali¹,

Padmanaban²,

Twala³

et al. 2017

Energies

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Abstract:In this paper, the approach focused on the variables involved in assessing the quality of a distributed generation system are reviewed in detail, for its investigation and research contribution. The aim to minimize the electric power losses (unused power consumption) and optimize the voltage profile for the power system under investigation. To provide this assessment, several experiments have been made to the IEEE 34-bus test case and various actual test cases with the respect of multiple Distribution Generation DG units. The possibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been verified. Finally, four algorithms were trailed: simulated annealing (SA), hybrid genetic algorithm (HGA), genetic algorithm (GA), and variable neighbourhood search. The HGA algorithm was found to produce the best solution at a cost of a longer processing time.

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Section: Introductionmentioning

confidence: 99%

Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms

Ali¹,

Padmanaban²,

Twala³

et al. 2017

Energies

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“…A parallel TS (PTS) [36] based method was proposed for feeder reconfiguration by Mori et al and TS algorithm was presented [37] for solving the problem of network reconfiguration in distribution systems in order to reduce [38] for loss-minimization reconfiguration in large-scale distribution systems. A modified TS (MTS) [39,40] based algorithm was used for reconfiguration of distribution systems using turning on/off sectionalizing switches to minimize active power losses. A number of artificial intelligent methods such as, GA [41,42], adaptive GA (AGA) [43] were proposed to reconfigure distribution feeders with the objective of minimizing real power losses while avoiding transformer and feeder overloads and inadequate voltages.…”

Section: Introductionmentioning

confidence: 99%

Oppositional krill herd algorithm for optimal location of capacitor with reconfiguration in radial distribution system

Sultana¹,

Roy²

2016

International Journal of Electrical Power & Energy Systems

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“…Distribution network reconfiguration (DNR) is a process of altering the topological structure of distribution feeders by changing the open/closed status of the sectionalizing and tie switches [1]. DNR is not limited to fault isolation; from time to time network reconfiguration is performed to achieve various goals, such as system loss reduction, overloads relieving [2], load aggregation [3,4], and system reliability improvement [5].…”

Section: Introductionmentioning

confidence: 99%

“…The performance and efficiency of any DNR algorithm largely rely on a wise combination of a smart topological coding strategy and an efficient searching algorithm. As a nondeterministic polynomial hard (NP-hard) problem, DNR has been heavily studied with various searching algorithms, from step-by-step heuristics, such as branch-exchange method [6], to metaheuristics based algorithms, such as tabu search (TS) [4], simulated annealing [7], genetic algorithm (GA) [8][9][10], and particle swarm optimization (PSO) [11]. Apart from the choice of searching algorithms, the distribution network representation or coding strategy is equally important due to the topological nature of DNR.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Algorithm of Particle Swarm Optimization and Tabu Search for Distribution Network Reconfiguration

Fang¹,

Zhang

2016

Mathematical Problems in Engineering

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This paper deals with the distribution network reconfiguration problem. A hybrid algorithm of particle swarm optimization (PSO) and tabu search (TS) is proposed as the searching algorithm. The new algorithm shares the advantages of PSO and TS, which has a fast computation speed and a strong ability to avoid local optimal solution. After a thorough comparison, network random key (NRK) is introduced as the corresponding coding strategy among various tree representation strategies. NRK could completely avoid the generation of infeasible solutions during the searching process and has a good locality property, which allows the new hybrid algorithm to perform to its fullest potential. The proposed algorithm has been validated through an IEEE 33 bus test case. Compared with other algorithms, the proposed method is both accurate and computationally efficient. Furthermore, a test to solve another problem also proves the robustness of the proposed algorithm for a different problem.

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A new intelligent optimization technique for distribution systems reconfiguration

Cited by 20 publications

References 12 publications

Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms

Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms

Oppositional krill herd algorithm for optimal location of capacitor with reconfiguration in radial distribution system

A Hybrid Algorithm of Particle Swarm Optimization and Tabu Search for Distribution Network Reconfiguration

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