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As far as the work related to the agriculture feeder segregation is concerned, no research has been initiated throughout the world in this area. Agriculture feeder segregation is a unique trick adopted in India that is based on its contemporary situation. However, ample research has been carried out worldwide in the area of network reconfiguration (NR), which is being considered as a very important task for real-time operation of the power distribution system. The essential role and performance of NR as well as the availability of a number of techniques for its implementation have made NR very useful for the distribution sector. NR has shown its suitability to be useful for the proposed objectives of this research proposal. The basic objectives of network reconfiguration include technical loss reductions, improving the voltage profile, minimizing outages, reducing device failures, minimizing overloading, smoothing peak demand, and increasing the network reliability. Some of the research work done worldwide in the area of network reconfiguration will be considered. From available data, Merlin and Back [9] proposed the concept of network reconfiguration for the first time in 1975. To determine the minimal-loss condition in a spanning tree structured distribution network, they made use of heuristic techniques. Similarly in 1997, it was found that the voltage instability problem in the distribution system, which spreads the corresponding transmission system, created major blackouts in the S/SE Brazilian system. This made the engineers think about an effective way of preventing this loss reduction, which included a network reconfiguration. Network reconfiguration is a non-linear optimization problem. Different researchers and industry persons throughout the world have made use of different optimization techniques to solve network reconfiguration problems. Various optimization techniques used to solve network reconfiguration problems include particle swarm optimization (PSO) [10], genetic algorithm (GA) [11], ant colony optimization [12], modified honey bee mating optimization (MHBMO) [13], branch exchange technique [14], hybrid heuristic technique [15], and fuzzy logic approach [16]. Most of the effective researches carried out in the area of network reconfiguration have made use of heuristic search techniques. Olamaei et al. [13] presented an efficient algorithm for multiobjective distribution feeder reconfigurations based on the modified honey bee mating optimization (MHBMO) approach. The main objectives of the distribution feeder reconfiguration (DFR) problem solved by this group are to minimize the real power loss and the deviation of the nodes’ voltage. Hemmatpour et al. [17] presented a novel reconfiguration mixed with distributed generation planning via considering the voltage stability margin. This describes a novel approach for optimum reconfiguration and optimal location of DGs in distribution networks based on a hieratical two-stage optimization problem to improve the power system voltage stability margin and reduce active power losses. Savier et al. [18] presented a method for the allocation of power losses to consumers connected to radial distribution networks before and after network reconfiguration in a deregulated environment. Loss allocation is made in a quadratic way, based on identifying the real and imaginary parts of the current in each branch. The network reconfiguration algorithm is based on the fuzzy multiobjective approach and the max–min principle is adopted for multiobjective optimization in a fuzzy framework. Wiselin et al. [15] proposed a hybrid technology to solve the distribution system reconfiguration problem. The mixture of the plant growth simulation algorithm (PGSA) and greedy and heuristic-based fuzzy operation has been used. The results obtained from the proposed optimization approach provide a detailed description of the state of different switches. A new distribution network reconfiguration approach using a tree model has been proposed by Dolatdar et al. [19]. From their research, it was shown that optimal reconfiguration involves the selection of the best set of branches to be opened, one each from each loop, for reducing resistive line losses and relieving overloads on feeders by shifting the load to adjacent feeders. However, since there are many candidate switching combinations in the system, the feeder reconfiguration becomes a complicated problem. A network reconfiguration technique that uses the distance measurement technique algorithm based on a two-stage methodology has been explained by Kashem et al [14]. Shirmohanunadi et al. [20] presented a reconfiguration of electric distribution networks for resistive line loss reduction. They described an efficient and robust heuristic method for the reconfiguration of distribution networks in order to reduce their resistive line losses under normal operating conditions. They claimed that their approach can be used in both the planning and operation environments because of its computational efficiency. Chiang et al. [21, 22] proposed an optimal network reconfiguration for distribution systems based on a two-stage solution methodology and a modified simulated annealing technique. They developed a solution algorithm to the network reconfiguration problem, which is a constrained, multiobjective, non-differentiable optimization problem. Their solution algorithm allows the designer to obtain a desirable, global, non-inferior point in a reasonable computation time. Nara et al. [11] presented implementation of a network reconfiguration using a genetic algorithm for a distribution system loss minimization. They concluded that NR is a complex mixed integer programming problem that is difficult to solve using a mathematical programming approach. Hence, they used a genetic algorithm as a search or optimization algorithm based on the mechanics of natural selection and natural genetics. Civanlar et al. [23] investigated the distribution feeder reconfiguration for loss reduction. They proposed a feeder reconfiguration as a planning and/or real-time control tool in order to restructure the primary feeders for loss reduction. Since electric utilities have become increasingly interested in the problems associated with interfacing of dispersed storage and generation (DSG), Rizy et al. [24] presented operational and design considerations for electric distribution systems with DSG. Three studies have examined the operational and planning problems associated with connecting small power sources to an electric distribution system. An efficient algorithm for load balancing of transformers and feeders by switching operation in large-scale distribution systems has been explained by Aoki et al. [25]. This work presents a systematic and practical algorithm for load balancing of transformers and feeders by automatic sectionalizing switch operation in large-scale distribution systems of a radial type. The algorithm is developed by extending an approximation algorithm for load transfer desired by two different transformers. Lee et al. [26] described and summarized the straightforward method developed and applied by Advanced Systems Technology (AST), a division of Westinghouse Electric Corporation, for The Pennsylvania Power and Light Company (PP&L). Liu [27] presented an expert system operational aid for restoration and loss reduction of the distribution sector. This work presents a new application of expert system techniques to the restoration of distribution systems. Primary feeders are typically radial in structure. To restore a maximal number of zones, several steps are followed: group restoration, zone restoration, and, if necessary, load transfer. Based on the methodology, an expert system was developed that can be utilized as an on-line aid to system operators in a distribution supervisory control and data processing system (SCADA) environment. Implementation of heuristic search strategies for distribution feeder reconfiguration is explained by Taylor [28] where a method is described for feeder reconfiguration with the potential for handling realistic operating constraints. The approach taken is to set up a decision tree to represent the various switching operations available. A best-first tree searching strategy, based on heuristics, is used to evaluate the various alternatives. Chan et al. [29] presented a methodology for determining the critical switches in the distribution system. The daily load curves of various types of load customers are used in a three-phase load flow program. The program calculates the hourly current loading of distribution feeders and current flows through all line switches. A distribution system comprising nine distribution feeders in the Taiwan Power Company (Taipower) is selected for computer simulation. Kennedy et al. [30] developed particle swarm optimization in which the evolution of several paradigms is outlined and implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.