CP组合神经网络在基于DGA的变压器绝缘故障诊断中的应用

对向传播(CP)算法是一种有教师学习和无教师学习算法的混合体,既具有良好的模式识别性能,又能很好地解决反馈型神经网络的收敛问题。笔者提出了基于CP分类器人工神经网络的变压器故障诊断方法,建立了CP组合神经网络模型,通过比较不同训练情况下的正判率来确定CP网络中的训练次数和竞争层神经元的个数。实例证明该模型诊断结果的正判率比改良电协研法和IEC三比值法有较大的提高,具有较高的诊断准确率和应用价值。

Application of CP Compound Modular Neural Network in DGA Based Power Transformer Fault Diagnosis

Counter propagation(CP) arithmetic is one kind mixture arithmetic complsed supervised learning algorithm and unsupervised learning algorithm.It has good pattern recognition performance,and can solve the convergence problem of feedback neural network.This paper presents a fault diagnosis method for power transformer based on CP neural network,and builds a compound neural networks model.The training times and the number of neurons on a competition level in CP network are determined by comparing the results in different training situations.The diagnostic examples indicate the diagnostic correctness of the proposed method is higher than that of the improved three rations method.