Main Chain Representation for Evolutionary Algorithms Applied to Distribution System Reconfiguration

Abstract

Distribution system problems, such as planning, loss minimization, and energy restoration, usually involve network reconfiguration procedures. The determination of an optimal network configuration is, in general, a combinatorial optimization problem. Several Evolutionary Algorithms (EAs) have been proposed to deal with this complex problem. Encouraging results have been achieved by using such approaches. However, the running time may be very high or even prohibitive in applications of EAs to large-scale networks. This limitation may be critical for problems requiring online solutions.The performance obtained by EAs for network reconfiguration is drastically affected by the adopted computational tree representation. Inadequate representations may drastically reduce the algorithm performance. Thus, the employed representation for chromosome encoding and the corresponding operators are very important for the performance achieved. An efficient data structure for tree representation may significantly increase the performance of evolutionary-based approaches for network reconfiguration problems.The present paper proposes a tree encoding and two genetic operators to improve the EA performance for network reconfiguration problems. The corresponding EA approach was applied to reconfigure large-scale systems. The performance achieved suggests that the proposed methodology can provide an efficient alternative for reconfiguration problems.

Optimized allocation of power quality monitors in transmission systems: A multiobjective approach

“…The MEAT is a multiobjective evolutionary algorithm which is strongly elitist and has been used in this work with some adaptations concerning the original algorithm developed by [17]. Its motivation is the mapping of promising regions of the objective space where possibly solutions of Pareto Front 3 are found.…”

“…Applying the tournament for selection from subpopulations of levels of dominance is the main modification compared to the originally proposed algorithm [17]. Fig.…”

“…The PQM allocation (PQMA) is modeled as a Multiobjective Optimization Problem (MOP) which involves the simultaneous analysis of these two concurrent criteria. The Multiobjective Evolutionary Algorithm with Tables (MEAT) is investigated to solve this MOP, since it has performed adequately for other MOPs involving EPS [17]. The MEAT for PQMA finds a set of efficient solutions, with an adequate trade-off between both objectives, helping the utility to decide what the most feasible monitoring plan is, considering the funds available.…”

Algoritmo evolutivo computacionalmente eficiente para reconfiguração de sistemas de distribuição

“…O AE Multi-objetivo proposto trabalha com várias subpopulações em paralelo armazenadas em tabela [32,84], onde os melhores indivíduos para cada característica do problema são armazenados em sua respectiva subpopulação. Por essa razão, o AE desenvolvido é chamado de AE Multi-objetivo em Tabela.…”

“…Vários desses métodos são metaheurísticas, algoritmos baseados em um conjunto de conceitos heurísticos que definem um método aplicável a um extenso conjunto de diferentes problemas [24]. Alguns exemplos de metaheurística são: branch exchange [25,26], Simulated Annealing (SA) [27], Busca Tabu (BT) [28,29], Sistemas Especialistas [30], Algoritmos Evolutivos (AE) [31][32][33][34][35][36], Algoritmos baseados em Colônias de Formigas (ACF) [37], otimização por enxame de partículas (PSO) [38,39], busca em vizinhança variável (VNS) [40][41][42], Greedy Randomized Adaptive Search Procedure (GRASP) [43], busca dispersa [44,45], Estimations of Distributions Algorithms (EDAs) [46,47], etc. Dentre esses, os AEs têm sido a metaheurística mais explorada para o problema de reconfiguração de sistemas de distribuição.…”