No abstract

No abstract

This paper presents a new method to reduce the distribution system loss by feeder reconfiguration. This new method combines self-adaptive particle swarm optimization (SAPSO) with shuffled frog-leaping algorithm (SFLA) in an attempt to find the global optimal solutions for the distribution feeder reconfiguration (DFR). In PSO algorithm, appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort. Thus, a self-adaptive framework is proposed to improve the robustness of PSO. In SAPSO the learning factors of PSO coevolve with the particles. SFLA is combined with the SAPSO algorithm to improve its performance. The proposed algorithm is tested on two distribution test networks. The results of simulation show that the proposed algorithm is very powerful and guarantees to obtain the global optimization in minimum time.self-adaptive particle swarm optimization (SAPSO), discrete particle swarm optimization (DPSO), binary particle swarm optimization (BPSO), shuffled frog-leaping algorithm (SFLA), evolutionary algorithms (EA), distribution feeder reconfiguration (DFR) Citation:Niknam T, Azad Farsani E. A hybrid evolutionary algorithm for distribution feeder reconfiguration. Sci China Tech Sci, 2010, 53: 950−959,

No abstract

In this paper, an efficient Tribe-Modified Shuffled Frog Leaping Algorithm (T-MSFLA) is presented to solve Multi-objective Distribution Feeder Reconfiguration (MDFR) problem with regard to Distributed Generator (DG) units. The Distribution Feeder Reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DG units. The main focus of this study is to minimize the real power loss, deviation of the nodes' voltage, and emission produced by DG units and distribution companies (substation buses), and total cost of the active power generated by DG units and distribution companies, so that radiality of the network is maintained. In this paper, the max-min method has been used to preserve the radiality of network. Because of the fact that the objectives are different and no commensurable, it is difficult to solve the problem by conventional approaches that may optimize a single objective problem. This paper presents a new T-MSFLA algorithm for the MDFR problem. In the proposed algorithm (T-MSFLA) at first we modify the frog leaping rule of shuffled frog leaping algorithm, the modified algorithm is MSFLA, and then a Tribe-MSFLA is used to prevent the prematurity of MSFLA. We consider an external repository to save non-dominated solutions found during the search process. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository within the limits. The proposed algorithm is tested on three distribution test feeders. The results of simulation show that the proposed method is very powerful and guarantees to obtain the global optimization.

SUMMARYDistributed feeder reconfiguration (DFR) is an operation problem, which entails altering the topological structure of the distribution feeders by rearranging the status of switches in order to obtain an optimal configuration. This paper proposes a new evolutionary algorithm (EA) for solving the multi-objective distribution feeder reconfiguration (MDFR) problem. The proposed algorithm is an efficient multi-objective modified shuffled frog leaping algorithm (MMSFLA) that has been used to solve MDFR problem. A new frog leaping rule is proposed to improve the local exploration of the SFLA, which in turn improves the overall performance of the MSLFA. The main focus of this study is to minimize the real power loss, deviation of the nodes' voltages, and the number of switching operations. In the proposed MMSFLA, an external repository is considered to save non-dominated solutions found during the search process. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository within the limits. Three distribution test feeders are considered to evaluate the feasibility and effectiveness of the proposed approach.

Summary Connection of distributed generation to the grid and the expansion of transmission system in order to meet the growing for electricity are causing the extent and complexity of the network structure. The occurrence of fault in such networks leads to flow large short circuit currents through the system, which may exceed the rating of existing circuit breakers and can damage system equipment. The utilization of fault current limiters (FCLs) in power systems can be an effective method to limit fault currents. FCLs can offer many benefits, but these benefits depend on the number, installation location, and impedance of FCL. In this paper, determining the number, location, and impedance of FCLs in network is modeled as a new optimization problem when objective functions are reliability, power loss, and economical use of FCLs. Also, a combination of discrete and continuous particle swarm optimization algorithm is employed to solve problem. Modified RBTS 2 bus test system is considered to evaluate the effectiveness and feasibility of the proposed method. Obtained results show the importance of finding optimum number, location, and impedance of FCLs in minimization of the proposed objective functions. Copyright © 2014 John Wiley & Sons, Ltd.

No abstract