Application of Grey Correlation Analysis in Evolutionary Programming for Distribution System Feeder Reconfiguration

Abstract

Feeder reconfiguration is a common technique that is used by distribution system operators during normal or emergency operational planning. By changing the status of switches on the distribution systems, the feeders can be reconfigured. During a feeder reconfiguration, more than one objective is considered by the distribution system operators. Due to the complexity of the reconfiguration problems, the system operators are looking for assistance from computer program that can provide adequate switching plans to reconfigure the feeders such that the desired goal can be achieved. Thus, the feeder reconfiguration is a type of discrete multi-objective optimization problems. Evolutionary programming (EP) technique is a method that can be applied to identify an optimal switching plan for feeder reconfiguration. A fitness function is required in EP for chromosome selection during reproduction process. The fitness function needs to integrate the objectives to provide a measure for each chromosome. Normalizing the objectives is a typical method for multi-objective optimizations such that these objectives are comparable. In this paper, Gray CoRrelation Analysis (GCRA) method is proposed. The proposed method is used to integrate the objectives and provide a relative measure to a particular switching plan associated with a chromosome without any prior knowledge of the system under reconfiguration. Two different distribution systems are used in this paper to demonstrate how the proposed GCRA is applied during the selection process of EP. Several simulations show that the EP can identify the solution more accurately when GCRA is applied than other methods.

Sequential quadratic programming particle swarm optimization for wind power system operations considering emissions

“…GHGs are CO 2 and NO 2 in this paper Equation (13) represents the fuel cost function of thermal generators. Equation (14) expresses the GHGs emission cost function.…”

“…Different heuristic techniques have been developed to solve the classical ED problems with constraints, to namely simulated annealing (SA) [11], genetic algorithm (GA) [12], evolutionary programming (EP) [13,14], tabu search (TS) [15], pattern search (PS) [16], particle swarm optimization (PSO) [17,18], as well as differential evolution (DE) [19,20]. Based on our experience, when compared with other approaches, the PSO is computationally inexpensive in terms of memory and speed.…”

Distribution network reconfiguration validation with uncertain loads – network configuration determination and application

“…Generally these methods can be grouped into several categories; classic optimization technique [10][11][12][13], sensitivities analysis method [14], knowledge-based heuristic method [15][16][17][18], and Genetic Algorithms [19]. Sensitivities analysis method and knowledge-based heuristic method can provide practical results with short computing time but may not be global solutions.…”

Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

“…Additionally, this approach alters the essence of the original technical problem. On the other hand, some authors have studied this problem using aggregation functions, converting the multi-objective problem into a single objective one that assumes a (weighted or not) sum of the selected objective functions [23][24][25][26][27][28][29][30]. The major difficulty in this kind of problem consists in the incompatibility of different criteria.…”

“…Most authors have used different well known heuristics (branch exchange [2,3,21], branch and bound [1,4], simulated annealing [5]), other heuristic rules or meta-heuristics [7][8][9][11][12][13]15,17,22,23,25,27,28] or multi-agent technologies [20]. On the other hand, some authors have developed methods based on evolutionary computation techniques [6,14,16,18,19,24,26,29,30]. An important drawback of these methods is the fact that they solve the reconfiguration problems as single objective problems.…”