Optimal network reconfigurations in distribution systems. II. Solution algorithms and numerical results

Chiang, Hsiao‐Dong; Jean-Jumeau, R.

doi:10.1109/61.58002

Cited by 342 publications

(154 citation statements)

References 6 publications

Supporting

Mentioning

131

Contrasting

Unclassified

Order By: Relevance

“…This result represents the optimal configuration and is confirmed in [9] and [11]. Another example is given in [5]. Before the reconfiguration, the total active power losses are 20.87 kW.…”

Section: Simulation Resultssupporting

confidence: 58%

“…Before the reconfiguration, the total active power losses are 20.87 kW. This value is different from the 69.76 kW value which was presented in [5]. After reconfiguration, the total active power losses are 9.43 kW.…”

Section: Simulation Resultsmentioning

confidence: 43%

“…In order to avoid the evaluation of the entire space of the candidate solutions and to minimize the computation burden, several algorithms have been developed. Most authors have used different well known heuristics (branch exchange [2,3,21], branch and bound [1,4], simulated annealing [5]), other heuristic rules or meta-heuristics [7][8][9][11][12][13]15,17,22,23,25,27,28] or multi-agent technologies [20]. On the other hand, some authors have developed methods based on evolutionary computation techniques [6,14,16,18,19,24,26,29,30].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

et al. 2013

View full text Add to dashboard Cite

Reconfiguration, by exchanging the functional links between the elements of the system, represents one of the most important measures which can improve the operational performance of a distribution system. The authors propose an original method, aiming at achieving such optimization through the reconfiguration of distribution systems taking into account various criteria in a flexible and robust approach. The novelty of the method consists in: the criteria for optimization are evaluated on active power distribution systems (containing distributed generators connected directly to the main distribution system and microgrids operated in grid-connected mode); the original formulation (Pareto optimality) of the optimization problem and an original genetic algorithm (based on NSGA-II) to solve the problem in a non-prohibitive execution time. The comparative tests performed on test systems have demonstrated the accuracy and promptness of the proposed algorithm.

show abstract

“…This result represents the optimal configuration and is confirmed in [9] and [11]. Another example is given in [5]. Before the reconfiguration, the total active power losses are 20.87 kW.…”

Section: Simulation Resultssupporting

confidence: 58%

Section: Simulation Resultsmentioning

confidence: 43%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

et al. 2013

View full text Add to dashboard Cite

show abstract

“…The developed methodology is tested with bus 2 of the RBTS (Allan et al, 1991) and a 69-bus distribution system (Chiang & Jean-Jameau, 1990). …”

Section: Case Studymentioning

confidence: 99%

Network Reconfiguration for Reliability Worth Enhancement in Distribution Systems by Simulated Annealing

Sirisumrannukul¹

2010

Simulated Annealing, Theory With Applications

View full text Add to dashboard Cite

“…As stated above it is not a problem specific, has less hold on initial solution point and tendency to solve large-scale and any kind of complex engineering problem, the researchers are motivated to solve distribution network reconfiguration (DNR) problem. It can be mathematically formulated as an optimization problem subjected to various operational constraints to ascertain radiality in the distribution network optimally that reduces the power loss and minimizes load balancing index [2][3] or maximize the benefits under the normal operation conditions [4].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Weighted Improved Discrete Particle Swarm Optimization for Minimizing Load Balancing Index in Radial Distribution Network

Subramaniyan¹,

Subramaniyan²,

Veerasamy³

et al. 2017

IJIES

View full text Add to dashboard Cite

Abstract:In this paper a metaheuristic based newfangled adaptive weighted improved discrete particle swarm optimization (AWIDPSO) algorithm is applied to minimize the load balancing index in radial distribution network reconfiguration (RDNR) problem. It is devised as extremely nonlinear and multimodal optimization problem under practical constraints. In order to improve the solution quality the constraint violations are augmented with objective function. Further, adaptively varying inertia weight increases the possible solution in the global search space and the proposed algorithm has obtained the optimal solution within lesser executing time. In this study, 33-bus system is analyzed for optimal network reconfiguration using the developed framework. Comparison of the simulated results with the results of well known prudent optimization technique confirms the applicability of the AWIDPSO algorithm for RDNR problem.

show abstract

Optimal network reconfigurations in distribution systems. II. Solution algorithms and numerical results

Cited by 342 publications

References 6 publications

Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

Network Reconfiguration for Reliability Worth Enhancement in Distribution Systems by Simulated Annealing

Adaptive Weighted Improved Discrete Particle Swarm Optimization for Minimizing Load Balancing Index in Radial Distribution Network

Contact Info

Product

Resources

About