基于RBF神经网络的干式空心电抗器涡流损耗计算

doi:10.19595/j.cnki.1000-6753.tces.170477

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Abstract

Based on numerical simulations,the structural parameters of dry-type air-core reactor were analyzed for the effect on eddy current losses. A unified model in engineering practice was then proposed to consider the fit scheme of winding cross section, the shape of conductor cross section, the airway width, and the number of layers per package. In order to improve the computational accuracy of reactor eddy current losses, a radial basis function(RBF) neural network model was established, in which the exponential function was determined as the activation function according to the relationship between the input and output variables. Moreover, an improved particle swarm algorithm for optimizing network parameters was presented. Numerical results indicate that the proposed model exhibits the highest precision and best computational performance. As a result, this model applies especially to the optimum design of dry-type air-core reactors.

Key words： AC resistance dry-type air-core reactor radial basis function neural network particle swarm algorithm

Received: 19 April 2017 Published: 13 June 2018

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TM472

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	ChenFeng
	WangJiawei
	Wu Menghan
	Ma Xikui

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ChenFeng,WangJiawei,Wu Menghan等. Eddy Current Loss Calculation of Dry-Type Air-Core Reactor Based on Radial Basis Function Neural Network[J]. Transactions of China Electrotechnical Society, 2018, 33(11): 2545-2553.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.170477 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I11/2545

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