A genetic algorithm based on the edge window decoder technique to optimize power distribution systems reconfiguration

• Published in: International journal of electrical power & energy systems Vol. 45; no. 1; pp. 28 - 34
• Main Authors: Torres, J., Guardado, J.L., Rivas-Dávalos, F., Maximov, S., Melgoza, Enrique
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.02.2013; Elsevier
• Subjects: Applied sciences; Combinatorial analysis; Connection and protection apparatus; Decoders; Distribution system reconfiguration; Edge window decoder technique; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Genetic algorithms; Loss reduction; Miscellaneous; Networks; Operation. Load control. Reliability; Optimization methods; Power distribution; Power networks and lines; Reconfiguration; Spanning trees; Substations; Switching theory; Topology; Edge window decoder technique; Distribution system reconfiguration; Loss reduction; Spanning trees; Optimization methods; Genetic algorithms; System configuration; Combinatorial problem; Optimization method; Network architecture; Combinatorial optimization; Load flow; Topological structure; Coding; Power flow; Power losses; Reconfigurable architectures; Selector switch; Distribution network; Large scale system; Substation; Decoding; Power distribution systems; Electrical network; Genetic algorithm; Heuristic method; Numerical simulation; Comparative study
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2012.08.075

► New genetic algorithm for power distribution system reconfiguration and loss reduction. ► Based in an efficient encoding strategy (EWD) suitable for building up spanning tree. ► A sensitivity study about the impact of genetic operators on the solution was carried out. ► The algorithm was successfully tested against other heuristic and meta-heuristic techniques. This paper describes a genetic algorithm developed for power distribution system reconfiguration with minimal losses. The reconfiguration problem consists in identifying a new network topology with minimal power losses, while all the electrical system constraints are satisfied like radial topology, lines and substations power flow below capacity limits, node voltage magnitude within limits and all nodes connected. This is a combinatorial optimization problem where the aim is to determine the final status, open/closed, of all switches in a large scale distribution system. The genetic algorithm developed uses the edge window decoder encoding technique for network representation and building up spanning trees, as well as efficient genetic operators in order to explore the search space. Using two representative distribution system configurations, the results obtained with the developed methodology are compared with those obtained with other heuristic and metaheuristic techniques. The numerical results presented show the usefulness and effectiveness of the proposed algorithm.