Published in IET Generation, Transmission & Distribution Received on 28th September 2009 Revised on 25th April 2010 doi: 10.1049/iet-gtd.2010.0056 ISSN 1751-8687 Multi-objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework N. Gupta 1 A. Swarnkar 1 K.R. Niazi 1 R.C. Bansal 2 1 Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, India 2 School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia E-mail: rcbansal@ieee.org Abstract: This study presents an efficient method for the multi-objective reconfiguration of radial distribution systems in fuzzy framework using adaptive genetic algorithm. The initial population for genetic algorithm is created using a heuristic approach and the genetic operators are adapted with the help of graph theory to generate feasible individuals. This avoids tedious mesh check and hence reduces the computational burden. The effectiveness of the proposed method is demonstrated on 70-bus test system and 136-bus real distribution system. The simulation results show that the proposed method is efficient and promising for multi-objective reconfiguration of radial distribution systems. 1 Introduction The reconfiguration of radial distribution systems (RDSs) is an effective and efficient means to minimise the distribution network line losses, improve voltage profile, manage load congestion and enhance system reliability. The reconfiguration of distribution systems is performed by managing the open/ close status of sectionalising and tie-switches to achieve certain objectives while satisfying operational constraints and radiality constraint with all loads energised. Extensive research work has been carried out in the area of reconfiguration of RDS. These research efforts can be broadly classified into conventional approaches and artificial intelligence (AI)-based approaches. Merlin and Back [1] were the first to report a heuristic method for distribution system reconfiguration to minimise line losses. They formulated the problem as a mixed integer non-linear optimisation problem and solved it through a discrete branch-and-bound technique. Afterwards some other conventional approaches [2–7] were developed, which include heuristic and classical optimisation techniques. In the area of AI-based approaches, Nara et al. [8] introduced a genetic algorithm (GA) technique for reconfiguration of RDS with minimum loss. Later various GA-based methods [9–12] have been developed for reconfiguration of RDS. Mendoza et al. [13] proposed a new methodology for minimal loss reconfiguration using GA with the help of fundamental loops. They restricted the search space of GA by modifying the genetic operators. Enacheanu et al. [14] presented a method based on GA for the loss minimisation in distribution networks, using matroid theory and graph theory. Su et al. [15] introduced an ant colony search (ACS) algorithm to solve the optimal network reconfiguration problem for power loss reduction. Hong and Ho [16] suggested a method based on GA to determine the network configuration and formulated it as a fuzzy multi-objective problem. Prufer number is used in GA to ensure the radial structure. Huang [17] presented an enhanced GA-based fuzzy multi-objective approach to solve the reconfiguration problem in RDS. Das [18] suggested a fuzzy multi-objective approach for feeder reconfiguration which incorporates a heuristic rule base. In [19–25] various AI techniques, like GA, ant colony optimisation (ACO), Tabu search, particle swarm optimisation (PSO) and so on, were investigated for multi- objective reconfiguration of RDS. PSO and GA are population-based meta-heuristic optimisation techniques and have potential to provide optimal or near-optimal 1288 IET Gener. Transm. Distrib., 2010, Vol. 4, Iss. 12, pp. 1288–1298 & The Institution of Engineering and Technology 2010 doi: 10.1049/iet-gtd.2010.0056 www.ietdl.org