Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A fuzzy heuristic algorithm for distribution systems' service restoration
Fuzzy Sets and Systems - Special issue on applications of fuzzy theory in electronic power systems
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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This paper presents an innovative method to solve the reconfiguration problem in a distribution network. The main motivation of this work is to take advantage of the power flow analysis repetition when reconfiguration leads the network to a previous configuration due to cyclical loading pattern. The developed methodology combines an optimization technique with fuzzy theory to gain efficiency without losing robustness. In this methodology, the power flow is estimated by well-trained neo-fuzzy neuron network to achieve computing time reduction in the evaluation of individuals during evolutionary algorithm runs. It is noteworthy that the proposed methodology is scalable and its benefits increase as larger feeders are dealt. The effectiveness of the proposed method is demonstrated through examples. The overall performance achieved in the experiments has proved that it is also proper to real time context.