A genetic algorithm optimization of radial basis function neural network prediction model for the prediction of transformer temperature rising is presented.Firstly,it uses the GA algorithm to optimize the RBF neural network initial value of four parameters including the number of hidden layer nodes,the output weights,the hidden layer basis function centers and width,then it uses the optimized RBF neural network to train samples,which overcomes the random parameters of the traditional neural network.Taking an S9-1000/10 low-loss power transformer as an example for the temperature rising test,the predicted values are compared with measured values and the values based on traditional BP neural network prediction.The results show that transformer temperature rise predicted values using this method is closer to measured values,and this prediction model has better accuracy and adaptability.
