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Aiming at the shortcomings of low accuracy and poor applicability of the traditional ground fault line selection method for small current grounding systems, an BP neural network algorithm based on genetic simulated annealing algorithm (GSAA-BP) is used to select the fault line. This algorithm not only avoids the extortionate proportion of the initial weight and threshold of the traditional BP neural network, but also improves the population diversity by changing the crossover and mutation probability of the genetic algorithm. It avoids the result of the algorithm converge to a local optimum. Comparing with other BP neural network algorithms shows that this method in training has higher convergence speed, better complex adaptability and more accurate judgment accuracy.

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