A fault type identification method of a transmission line based on convolution neural network

Abstract

The invention discloses a fault type identification method of a transmission line based on a convolutional neural network. A convolutional neural network algorithm belongs to a deep learning algorithm. The deep learning algorithm is applied to the field of fault type identification of transmission lines, manual extraction of fault features is not needed for fault type identification, and a conventional line fault type identification method based on an artificial intelligence algorithm needs extraction of fault features in advance, so the invention simplifies the structure of fault type identification. The invention improves the identification efficiency of the line fault type identification, and in the application of the line fault type identification algorithm based on the deep learning,a plurality of parameters will cause the algorithm to be different in the training process, and the invention intends to optimize the line fault type identification algorithm. The method reduces the error rate of line fault type identification, and different activation functions can make the training error completely different. The method uses different activation functions to train the line faulttype identification, and finds the optimal activation function.

Description

technical field [0001] The invention relates to the field of fault type identification of transmission lines, in particular to a method for identifying fault types of transmission lines based on a convolutional neural network. Background technique [0002] Among the various types of faults in transmission lines, short-circuit faults occur most frequently and cause serious damage. When the short-circuit accident is serious, it will cause a large area of ​​melting of the metal conductor, and when it is particularly serious, there will be splashing, which will eventually lead to some disastrous consequences, such as the occurrence of fire. In addition, short-circuit faults will also cause the voltage of the power system to generally decrease, and even make some users' power supply unsafe. Short-circuit faults usually also change the stability of the power system, and in severe cases may cause large-scale blackouts. Even some short-circuit situations will interfere with the co...

AI Technical Summary

Problems solved by technology

In this way, the selection of fault types is realized, but because the traditional artificial neural network is suitable for small sample training, and certain fault features need to be extracted in advance, otherwise it is not conducive to training, and for larger samples, it is somewhat lacking in training time and accuracy

Images