Abstract
In order to improve the accuracy of transformer fault diagnosis, this paper presents a fault diagnosis method based on Bat algorithm to optimize randomized neural network. Because the initial connection weights and hidden neuron biases of the randomized neural network are generated randomly, the network results are easily unstable. Therefore, based on the excellent performance of at algorithm in function optimization, this paper uses the improved Bat algorithm to optimize the weight of randomized neural network. In order to overcome the shortcomings of early convergence and poor results of Bat algorithm, the inertia weight of particle swarm optimization (PSO) is introduced into Bat algorithm, and the velocity update formula is improved. In order to improve the diversity of population and the local search ability of the Bat algorithm, the chaotic local search method is added to balance the global search ability of the Bat algorithm in the early stage and the local search ability in the later stage. In order to control the range of bat’s later position, the empirical factor is introduced into the position change formula to accelerate the convergence speed of the algorithm. Finally, the empirical results show that the proposed fault identification model can better diagnose various types of transformer faults.
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Yan, C., Li, M., Liu, W. (2020). Power Transformer Fault Diagnosis Based on Improved Bat Algorithms to Optimize RNN. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_58
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DOI: https://doi.org/10.1007/978-3-030-32456-8_58
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