Multi objective distribution Feeder Reconfiguration: using fuzzy sets in normalization of objective functions

The reconfiguration is an operation process used for optimization with specific objectives by means of changing the status of switches in a distribution network. This paper presents an algorithm for network recon-figuration based on the heuristic rules and fuzzy multi objective approach where each objective is normalized with inspiration from fuzzy set to cause optimization more flexible and formulized as a unique multi objective function. Also, the genetic algorithm is used for solving the suggested model, in which there is no risk of nonlinear objective functions and constraints. The effectiveness of the proposed method is demonstrated through several examples in this paper. DOI: 10.4018/jaec.2010040103 International Journal of Applied Evolutionary Computation, 1(2), 60-73, April-June 2010 61 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. operation the most improved answer is selected as a final one. So far, several different techniques have been presented to resolve the network reconfiguration problem. For instance, Civanlar et al. in (Civanlar, Grainger, Yin, & Lee, 1998) described a formula to estimate the loss change resulting from the transfer of a group of loads from one feeder to another feeder. In Baran and Wu (1989) a heuristic algorithm was employed to minimize the power loss and load balancing, while Liu, Lee, and Vu (1989) developed a global optimality condition for the problem and two solution algorithms. Shirmohammadi et al. (Shirmohammadi & Hong, 1989) described a technique for the reconfiguration of distribution networks to decrease their resistive line losses and included results pertaining to large scale system examples. In Wang, Chiang, and Darling (1996) explicit loss reduction and line flow formulas were developed to determine efficiently the switching operations. Recently, AI-based approaches have been described to solve the problem and the results of such methods as the expert system (Liu, Lee, & Venkata, 1988), Genetic algorithm (Nara, Shiose, Kitagawa, & Ishihara, 1992), neural network (Kim, Ko, & Jung, 1993), simulated annealing (Chang & Kuo, 1994), evolutionary programming (EP) (Lin, Cheng, & Tsay, 1999), and fuzzy logic (Zhou, Shirmohammadi, & Liu, 1997; Prasad, Ranjan, Sahoo, & Chaturvedi, 2005) are all encouraging. Where their methodologies in (Zhou, Shirmohammadi, & Liu, 1997) combined the optimization techniques with heuristic rules and fuzzy logic for efficiency and robust performance and (Prasad, Ranjan, Sahoo, & Chaturvedi, 2005) presented a fuzzy mutated genetic algorithm for optimal reconfiguration of radial distribution systems. This method involves a new chromosome representation of the network and a fuzzy mutation control for an effective search of solution space. As it is clear, in the most introduced methods a uni-objective function is considered for solving the problem and due to the importance of the power loss minimization, reconfiguration with the purpose of reduction in loss is highly signified. In this essay four objectives, included in one multi-objective function with weight factor related to each objective are considered as following: 1. Minimization of the system’s power loss; 2. Minimization of the deviation of nodes voltage; 3. Minimization of the branch current constraint violation; 4. Load balancing among various feeders. For normalization of each function, fuzzy sets are used to make reconfiguration more flexible not only to satisfy realistic objectives and soft constraints, but also to supply rigorous mathematical and heuristic approaches within the problem-solving process. The fuzzy set theory provides an excellent framework for integrating the mathematical and heuristic approach into a more realistic formulation of the reconfiguration, while keeping an efficient computation (Huang, 2002). Owing to the non-commensurable characteristics of the objectives, a conventional approach that optimizes a single objective function is inappropriate for this problem. The fuzzy sets is therefore adopted to simultaneously consider the multiple objectives and to obtain a fuzzy satisfaction maximizing decision. Meanwhile, Genetic algorithm is considered to be an efficient method for solving the large-scale combinatorial optimization problem, due to its ability to search global or near global optimal solutions and its appropriateness for parallel computing in which there is no risk of non-linear objective functions and constraints. Finally the numerical results of the proposed method used on a simple network are provided for more study. 2. ProBleM forMulation As mentioned earlier, reconfiguration of distribution feeders is done by means of changing the 12 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/heuristic-approach-multi-objectivedistribution/43540?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Computer Science, Security, and Information Technology. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2