Power System Fault Probability Diagnosis Based on the Logistic Regression Deep Neural Network

As one of the standard modules of SCADA,the fault diagnosis software still has the problem of high misrecognition rates. To address this issue,this study proposes a new fault diagnosis model and algorithm based on the logistic regression deep neural network to improve accuracy. The building process of the new model and algorithm is as follows. First,a regression deep neural network (DNN)is established for each equipment,with fault feature vector whose elements have all been transformed into 1 or -1 as the input,the fault probability of the corresponding equipment as the output,and the backpropagation method based on RMSprop as the training method. Then,to eliminate the difficulty caused by the need for a large sample size for training the DNN and the lack of actual fault history records,a new method is presented in this study to expand and achieve the required number of records for specific equipment. Given the fact that each type of equipment and its microenvironment have passed the rigorous tests before being operated,the record expanding method treats the historical fault records of the same type of equipment at the same substation or a nearby substation as the same or analogous,considerably increasing the number of historical records. On this basis,a sufficient sample size can be produced by the probability,statistical,and random sampling techniques and the model can be successfully trained. In the sample verification and analysis section,first,the correctness of the fault probability generated by the model is thoroughly verified using the simulation system. Then,a comprehensive comparison of the proposed method,the method based on the expert system,and the method based on the shallow neural network (one hidden layer)using the cases from practical power systems is performed. Results show that the proposed method reduces the training time by approximately 7% (i.e.,from 4.05s to 3.77s),the test error by approximately 33% (i.e.,from 0.0051 to 0.0034)compared with the method based on the shallow neural network,and the misrecognition rate from 31.25% to approximately 0 compared with the method based on the expert system. Because of its easy implementation and high accuracy,the proposed method has considerable potential for application in practical power systems and can increase the accuracy of fault diagnosis. © 2021, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.