Abstract:In order to improve the management level of the low-voltage transformer area and guide the grass-roots operations and maintenance staffs found the transformer area problems in time, a comprehensive evaluation method of the operation state of the low-voltage transformer area based on the radial basis function neural network is proposed. In this method, firstly, the improved k-means algorithm is used to classify the low-voltage transformer area, then the evaluation index system set of the operation state of the transformer area is obtained, the comprehensive order analysis and the weighted sum calculation of the historical transformer area evaluation are carried out. Finally, taking the evaluation value as the training sample, we get the radial basis function neural network evaluation model, and use the model to carry out the state evaluation in the operation area. The example analysis shows that the comprehensive evaluation method can effectively carry out the operation state evaluation of the transformer area, timely find out the fault or abnormal problem of the line and metering equipment, and provide technical support for the operation problem management of the low-voltage transformer area and the improvement of the management level of the transformer area.