Artificial immune algorithm applied to distribution system reconfiguration with variable demand. (November 2016)

• Record Type: Journal Article
• Authors: Souza, Simone S.F.; Romero, Ruben; Pereira, Jorge; Saraiva, João T.
• Is Part Of: International journal of electrical power & energy systems. Volume 82(2016:Nov.)
• Page Start: 561
• Page End: 568
• Publication Date: 2016-11
• Subjects: Distribution system reconfiguration -- Variable demands -- Artificial immune systems -- Copt-aiNet; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2016.04.038
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: We apply the Copt-aiNet algorithm to the reconfiguration of distribution systems. In the resolution of the reconfiguration problem were considered variable demands. The method presented high-computing performance. An comparison of the results obtained was performed with the results available in the literature. Results showed that the proposed methods are efficient. Abstract: This paper presents a new methodology to solve the reconfiguration problem of electrical distribution systems (EDSs) with variable demand, using the artificial immune algorithm Copt-aiNet (Artificial Immune Network for Combinatorial Optimization). This algorithm is an optimization technique inspired by immune network theory (aiNet). The reconfiguration problem with variable demand is a complex problem of a combinatorial nature. The goal is to identify the best radial topology for an EDS in order to minimize the cost of energy losses in a given operation period. A specialized sweep load flow for radial systems was used to evaluate the feasibility of the topology with respect to the operational constraints of the EDS and to calculate the active power losses for each demand level. The algorithm was implemented in C++ and was evaluated using test systems with 33, 84, and 136 nodes, as well as a real system with 417 nodes. The obtained results were compared with those in the literature in order to validate and prove the efficiency of the proposed algorithm.