基于贝叶斯和模糊L-M网络的变压器故障诊断

Transformer Fault Diagnosis Based on Bayesian and Fuzzy L-M Network

针对现今电力变压器故障诊断方法中存在编码不齐全、准确率不高等一系列问题,研究贝叶斯理论的神经网络算法,提出一种基于贝叶斯正则化优化L-M(Levenberg-Marquardt)算法神经网络的变压器油色谱故障诊断方法。算法采用贝叶斯方法确定超参数,使得神经网络在训练过程中能自适应地调节超参数的大小,得出目标函数的最优化参数。同时,该方法运用模糊理论对改良三比值法的边界模糊化,将得到的特征气体比值编码作为网络模型的输入,有利于去除冗余信息,并且克服了编码边界区间过于绝对的缺点。最后,运用仿真软件对典型变压器运行数据进行仿真,验证了该算法的可行性。结果表明,建立的模型对变压器进行故障诊断时迭代次数为21次,实际值与预测值的误差平方和仅为0.000 618。

According to the problems in the fault diagnosis method of power transformer, such as incomplete code, less accurate rate and so on, this paper studied the neural network algorithm of Bayesian theory, and proposed a transformer oil chromatographic fault diagnosis method which was based on the L-M （Levenberg-Marquardt） neural network optimized by Bayesian regularization algorithm. Firstly, the algorithm used Bayesian approach to determine the hyper parameters, which could make the neural network adaptively adjust the parameter in the training process and get the optimization parameters of the objective function. Secondly, the method used the fuzzy theory to handle the boundary of improved three-ratio method, and then the characteristic gas ratio code wa,~ obtained and used as the network model input, which had advantages to remove the redundant information and overcome the absolute of code boundary. Finally, this paper used the simulation software to simulate the operation data of typical transformer and verified the feasibility of the proposed algorithm. The results show that the iteration times is 21, and the error square sum of actual and predicted values is only 0. 000 618, when the proposed model is used for the fault diagnosis of transformer.