基于神经网络的变压器故障诊断

为了保障变压器在电力系统中能够安全有效的运行,提出了一种将RBF神经网络与模糊控制算法相结合对变压器进行故障诊断的方法。设计了具有6层神经网络的学习体系,并且将模糊隶属度函数引入到第2层中,加快了神经网络的学习速度。基于变压器故障的数据统计,通过对其内部的气体含量进行分析对故障类型进行分类。通过样本数据对所设计的模糊RBF神经网络进行故障诊断训练。实验结果表明,通过训练后的该模型对变压器的故障诊断具有更好的效果。

Fault diagnosis of transformer based on neural netwo

In order to ensure the safe and efficient operation of the transformer in the power system, this paper proposes a method to fault the transformer by combining the RBF neural network with the fuzzy control algorithm.The neural network learning system with 6 layers is designed, and the fuzzy membership function is introduced into the second layer, which accelerates the learning speed of the neural network. Data Based on Transformer Faults This paper analyzes the types of faults by analyzing the internal gas content. The fuzzy RBF neural network designed by this paper is used to diagnose fault diagnosis. The diagnostic results show that the diagnostic model has better effect on the fault diagnosis of the transformer.