Power transformer incipient fault diagnosis based on neutral network optimized by combined ant colony optimization

In order to solve the problems of BP neural network in transformer fault diagnosis, such as slow convergence speed and easy to fall into local minimum, combined ant colony algorithm (combined ACO,CACO) is proposed to optimize the weights and thresholds of BP neural network, to speed up convergence speed and to achieve global optimal. This method combines the elitist strategy ant system with the max-min ant system to limit the range of pheromone trajectories of elitist ants. It effectively solves the problems of large discrepancy between different pheromone trajectories to avoid local optimum and premature convergence and improves the global search ability of the algorithm. The combined ant colony algorithm neural network effectively overcomes the shortcomings of BP algorithm training neural network in transformer fault diagnosis. The example verification shows that CACO neural network reduces the number of iterations by 96% than that of BP neural network and the accuracy of CACO neural network reaches 93.9% in fault diagnosis which is far higher than that of 78.5% of BP neural network, it also proves the effectiveness of the CACO algorithm.

(from magazine Electrotechnical Application 6/2019 on theoretical analysis.)

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