关键词: 变压器噪声, RBF神经网络, 自适应算法, 粒子群算法, 改进梯度下降算法

Abstract: For the deficiency of the existing active noise control algorithms for transformers, a new algorithm for noise suppression is proposed, which is based on the combination of several algorithms, i.e. adaptive algorithm, particle swarm optimization, improved gradient descent algorithm and RBF neural network algorithm. Firstly, the algorithm applies the adaptive algorithm to determine the number of nodes and the corresponding parameters of the hidden layer of RBF neural network in system controller; Then, according to the switching strategy, particle swarm optimization or improved gradient descent algorithm are selected adaptively to optimize the node number and parameters; Finally, the optimized nodes and parameters of the hidden layer are fed back to the system controller. As a result, the infrasound source of the system is able to better offset the initial sound source. By comparing the improved RBF neural network proposed with the conventional RBF neural network and BP neural network, it is shown that the proposed algorithm can effectively improve the adaptive ability and the anti-interference capability of the system, by which the transformer noise can be controlled within the relatively low range and the improved ability of noise reduction is obtained.

Key words: transformer noise, RBF neural network, particle swarm optimization, improved gradient descent algorithm