A Convolutional Neural Network Based Fault Type Identification Method for Transmission Lines

Abstract

The invention discloses a method for identifying fault types of transmission lines based on a convolutional neural network. The convolutional neural network algorithm belongs to a type of deep learning algorithm. By applying the deep learning algorithm to the field of fault type identification of transmission lines, the fault type The identification does not need to manually extract fault features. In the past, line fault type identification based on artificial intelligence algorithm needs to extract fault features in advance. This invention simplifies the structure of fault type identification; improves the identification efficiency of line fault type identification, and the line fault type based on deep learning In the application of the recognition algorithm, numerous parameters will cause the algorithm to vary greatly in the training process, and the present invention intends to optimize it; reduce the error rate of line fault type recognition, and different activation functions will make the training errors completely different. The present invention uses Different activation functions are used to train it to find 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

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