Fast fault section estimation in distribution control centers using adaptive genetic algorithm

Abstract This paper presents a novel mathematical model for fast fault section estimation in a Distribution Control Center (DCC). The mathematical model is divided into two parts, namely: (1) a protection system operations model based on operator’s heuristic knowledge of the protection system performance and (2) an optimization Unconstrained Binary Programming (UBP) model based on parsimonious covering theory. In order to solve the UBP model, an Adaptive Genetic Algorithm (AGA) using crossing over and mutation rates that are automatically tuned in each generation is proposed. An Alarm Probabilistic Generator Algorithm (APGA) is developed and a real four-interconnected distribution substation system is used to test exhaustively the approach. Results show that the proposed methodology is capable of performing fault section estimation in a very fast and reliable manner. Furthermore, the proposed methodology is a powerful real-time fault diagnosis tool for application in future Distribution Control Centers.

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