基于改进粗糙集理论与概率神经网络的变压器故障诊断研究

提出了一种基于改进粗糙集理论与概率神经网络的变压器故障综合诊断方法.利用了粗糙集理论的决策表约简技术,去除冗余信息,并引入可辨识矩阵,更加快速地去除故障冗余属性,减小了约简过程的复杂度.将得到的最小决策表作为改进的概率神经网络的训练样本,提高了PNN的训练速度和诊断的准确率.实例证明,该模型不仅能在信息不完备的情况下进行有效诊断,而且可以提高诊断速率及正判率.

Study on Transformer Fault Dlagnosis Based on Improved Rough Set Theory and Probabilistic Neural Network

A synthetic fault diagnosis method based on improved rough set theory and probabilistic neural network for electric power transformer is proposed. The redundancy information is deleted by using decision table reduction technique of RS. The discernibility matrix is introduced into the reduction of decision table,which more quickly removes fault redundant attributes,and reduces complexity reduction process. The training stylebook of PNN is minimal decision table,thus the training speed and the accuracy of diagnosis are effectively improved. The diagnosis examples showthat the model can not only effectively diagnose incomplete information but also improve the diagnosis rate and correct rate.