A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration

Abstract

One of the very important way to save the electrical energy in distribution system is network reconfiguration for loss reduction. This paper proposes a new hybrid evolutionary algorithm for solving the distribution feeder reconfiguration (DFR) problem. The proposed hybrid evolutionary algorithm is the combination of SAPSO (self-adaptive particle swarm optimization) and MSFLA (modified shuffled frog leaping algorithm), called SAPSO–MSFLA, which can find optimal configuration of distribution network. In the PSO algorithm, appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort. Therefore, a self-adaptive framework is proposed to improve the robustness of the PSO, also in the modified shuffled frog leaping algorithm (MSFLA) to improve the performance of algorithm a new frog leaping rule is proposed to improve the local exploration of the SFLA. The main idea of integrating SAPSO and MSFLA is to use their advantages and avoid their disadvantages. The proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and guarantees to obtain the global optimization in minimum time.

Introduction

In the radially distribution system, the configuration may be varied to obtain a new network structure to reduce power loss, increase system security and enhance power quality. In these system there are many switches that they are divided into two types: sectionalizing-switch (normal closed) and tie-switch (normal open). The change (reconfiguration) in distribution system is performed by opening sectionalizing and closing tie switches so that the radiallity of the network is maintained and all of the loads are energized. The discrete nature of the switch values and radiallity constraint prevent the use of classical optimization techniques to solve the distribution feeder reconfiguration (DFR) problem. Therefore, the most of the algorithms in the literature are based on heuristic search techniques.

In recent years, considerable research has been conducted for loss minimization in the DFR. Kim et al. (1993) proposed a neural network-based method to identify network configurations corresponding to different load levels. Taylor and Lubkeman (1990) presented an expert system using heuristic rules to shrink the search space. Kashem et al. (1999) proposed “distance measurement technique algorithm” that finds a loop first, and then, to improve the load balancing, a switching scheme was determined in that loop. Jeon incorporated the simulated annealing algorithm with Tabu search for loss reduction in Jeon and Kim (2000). The Tabu search attempted to determine a better solution in the manner of a greatest-descent algorithm, but it could not give any guarantee for the convergence property. Lin et al. (2000) presented a refined genetic algorithm (RGA) to reduce losses. Morton and Mareels (2000) presented a brute-force solution for determining a minimal-loss radial configuration. The graph theory involving semi-sparse transformations of a current sensitivity matrix was used, which guarantees a globally optimal solution but needs an exhaustive search. Goswami and Basu (1992) proposed a power-flow-minimum heuristic algorithm for DER problem. Lopez and Opaso (2004) proposed a method for online reconfiguration. Das (2006) presented a fuzzy multi-objective approach to solve DFR. Niknam and New, 2009a, Niknam, 2009b presented two approaches based on norm2 for multi-objective distribution feeder reconfiguration. Niknam, 2009b proposed a hybrid approach based on DPSO, ant colony optimization and fuzzy system for reconfiguration of distribution system. Liu and Chen (2000) proposed a fuzzy genetic algorithm for DFR problem. Bi et al. (2002) proposed a refined genetic algorithm for distribution network reconfiguration. Shirmohammadi proposed the reconfiguration in electric distribution networks for resistive line loss reduction (Shirmohammadi and Hong, 1998). Li et al. (2007) proposed a hybrid particle swarm optimization approach for DFR problem. Yu et al. (2009) presented an improved genetic algorithm with infeasible solution disposing for DFR problem. Chiou proposed a method-variable scaling hybrid differential evolution (VSHDE) for solving the network reconfiguration for power loss reduction and voltage profile enhancement of distribution systems (Chiou et al., 2005). Su proposed a method for reducing power loss and enhancing the voltage profile by the improved mixed-integer hybrid differential evolution (MIHDE) method for distribution systems (Su and Lee, 2003). Cheng and Kou (1994) used simulated annealing for network reconfiguration in distribution system. Raju proposed an algorithm based on sensitivity and heuristics for minimum loss reconfiguration of distribution system (Viswanadha Raju and Bijwe, 2008). Ahuja proposed an AIS–ACO hybrid approach for multi-objective DFR problem (Ahuja et al., 2007).

In the distribution system, since there are many candidate switching combinations, the DFR problem is modeled as a complicated combinatorial, non-differentiable, constrained optimization problem. Therefore, it is difficult to solve the problem by conventional approaches and most optimal algorithms cannot effectively solve this kind of problem and they usually achieve local optimal solutions rather than global optimal solutions.

In this paper, a new hybrid algorithm is presented to find the optimal operating condition of the distribution networks. The algorithm is based on the combination of self-adaptive particle swarm optimization (SAPSO) and modified shuffled frog leaping algorithm (MSFLA).

The PSO algorithm has been recently proposed and proved as a powerful competitor to the other well-known algorithms in the field of optimization (Niknam, 2009b, Niknam, 2009c). Although PSO eventually determines the desired solution, its convergence rate is slow. Proper selection of parameters may increase the performance efficiency by itself. However, as in other evolutionary algorithms (EA), appropriate adjustment of its parameters is cumbersome and usually requires a lot of time. Thus, in this paper a self-adaptive framework is proposed for adjusting PSO algorithm’s parameters. In this paper, the algorithm’s parameters are coevolved with the particles. In proposed paper the (SAPSO) composed of two parts:

• Self-adaptive discrete particle swarm optimization (SADPSO) which determines the status of sectionalizing switch number.

• Self-adaptive binary particle swarm optimization (SABPSO) used for determining the status of the tie switches (open or close).

The SFLA which is a population-based optimization algorithm can be used for solving many complex optimization problems, which are nonlinear, non-differentiable and multi-modal. In this paper, a new frog leaping rule is proposed to improve the local exploration of the SFLA. The main idea behind the new frog leaping rule is to extend the direction and the length of each frog’s jump by emulating frog’s perception and action uncertainties. The modification widens the local search space, thus helps to improve the performance of the SFLA. The most prominent profit of MSFLA is its fast convergence speed (Elbeltagi et al., 2005). However, some limitations in MSFLA may slow down the convergence speed and even cause premature convergence.

Taking advantage of the compensatory property of SAPSO and MSFLA, we propose a new algorithm that combines the evolutionary natures of both algorithms (denoted as SAPSO–MSFLA). The robustness of proposed algorithm will be tested on two distribution test feeders and the results are compared with those obtained by other methods.