Distribution system reconfiguration for loss reduction: a new algorithm based on a set of quantified heuristic rules

Sarfi,; Salama, Salama; Chikhani,

doi:10.1109/ccece.1994.405645

Cited by 7 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over the last three decades, researchers and utility companies in the area of distribution systems automation and control have developed and utilized different techniques for reducing losses within the power distribution system level, these approaches include load balancing, capacitor placement, introduction of higher voltage level, reconductoring, reconfiguration etc., (Sarfi & Chikhani, 1994;Abdelaziz et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the introduction of new equipment at distribution level offers tremendous financial burden on the utilities and this cannot be justified by potential saving. Also, the use of fixed compensators offers optimal reduction in losses for specific demand condition but requires an efficient control system which is expensive (Abubakar et al, 2015;Sarfi & Chikhani, 1994). The choice of reconductoring is not an option due to the financial implication and time requires for relaying new feeders which might affect system availability and the quality of service.…”

Section: Introductionmentioning

confidence: 99%

“…The choice of reconductoring is not an option due to the financial implication and time requires for relaying new feeders which might affect system availability and the quality of service. In addition, the introduction of higher voltage level requires upgrading the rating of the transformer and other equipment in the network (Duan et al, 2015;Badran et al, 2017;Sarfi & Chikhani, 1994).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal reconfiguration of radial distribution networks using improved genetic algorithm

Abubakar¹,

Ekundayo²,

Olaniyan³

2019

Nig. J. Technol. Dev.

View full text Add to dashboard Cite

Reconfiguration of an electrical power radial distribution network is aimed at finding a radial operating structure that minimizes the system active power loss, enhancing the system voltage profile via reducing the active power losses whilst satisfying operating constraints is one of the most important measures of improving the operational performance of a distribution system. In this paper, an efficient approach to solving the problem of reconfiguration considering active power loss, total voltage deviation for a typical distribution network. The method developed is based on improved genetic algorithm to determine the optimal location of tie and sectionalizing switches, with a view to yield an optimal performance for the network. The reconfiguration model was implemented using MATLAB R2016a simulation environment. The effectiveness and validity of the proposed model was tested on a 16, 33 and 69 IEEE-Bus standard systems for distribution network. The results show that with a reduction of 8.86%, 31.15% and 53.53% in active power loss as compared to the initial configuration, whilst a total voltage deviation of 0.0284p.u., 0.0622p.u and 0.0517p.u for 16, 33 and 69 IEEE Bus respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal reconfiguration of radial distribution networks using improved genetic algorithm

Abubakar¹,

Ekundayo²,

Olaniyan³

2019

Nig. J. Technol. Dev.

View full text Add to dashboard Cite

show abstract

“…Network reconfiguration technique for losses reduction is presented in various bibliographies published since 1970. It started by reconfiguration through heuristic methods [3][4][5][6][7]. Then, reconfiguration methods developed involving artificial intelligence methods using optimization tools as genetic algorithms [8][9][10], ant colony [11] and particle swarm [12].…”

Section: Introductionmentioning

confidence: 99%

AI based reconfiguration technique for improving performance and operation of distribution power systems with distributed generators

Fayek

Sweif

2013

4th International Conference on Power Engineering, Energy and Electrical Drives

View full text Add to dashboard Cite

This paper targets the enhancement of distribution power system's performance and operation through reducing system's real losses and improving overall voltage profile of the network taking into consideration topological and load constraints. For this target, two main techniques are employed in this paper; network reconfiguration and distributed generators installation. The paper proposes genetic algorithm (GA) along with an analytically developed load flow code to generate optimal network topology and find optimal sizing, locations and number of distributed generators to be installed considering different DG types. To demonstrate the effectiveness of the research, the IEEE 33 bus system is considered in this paper. This is a three phase radial balanced distribution system. Index Terms--Distribution system, network reconfiguration, distributed generators (DG), genetic algorithm optimization (GA), power loss reduction, voltage profile improvement.

show abstract

Network radiality, parameter, and performance heuristics in optimization of power distribution system operations. Part 1: crisp rule base

Sarfi¹,

Solo

2002

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

Distribution system reconfiguration for loss reduction: a new algorithm based on a set of quantified heuristic rules

Cited by 7 publications

References 7 publications

Optimal reconfiguration of radial distribution networks using improved genetic algorithm

Optimal reconfiguration of radial distribution networks using improved genetic algorithm

AI based reconfiguration technique for improving performance and operation of distribution power systems with distributed generators

Network radiality, parameter, and performance heuristics in optimization of power distribution system operations. Part 1: crisp rule base

Contact Info

Product

Resources

About