Distribution network fault identification method and related device

Abstract

The invention discloses a distribution network fault identification method and a related device. The method comprises the following steps of constructing an input matrix according to a switching action of an input sample, setting a label for the fault type of the output sample according to the input sample, and constructing an output matrix according to the label of the output sample, acquiring a trained neural network model by performing forward transmission and back propagation training on the neural network model through an input matrix, wherein a loss function in the training process is a log-likelihood function, and a back propagation algorithm is a mixed algorithm combining root-mean-square propagation and learning rate attenuation, taking the input matrix and the output matrix as sample data, calculating the mean value and the standard deviation of current values of all the sample data, and after normalization preprocessing on the current data of the sample data through a z-score standardization method, inputting the current data into the trained neural network model for processing, and outputting a fault type corresponding to the current data. The problems of poor distribution network fault identification speed and poor accuracy in the prior art are solved.

Description

technical field [0001] The present application relates to the field of electric power technology, and in particular to a method and a related device for fault identification of a distribution network. Background technique [0002] With the rapid expansion of the grid scale, the grid structure is becoming more and more complex. Due to the increase of the equipment base, problems such as equipment aging and failure are inevitable, and the distribution network trip faults are gradually exposed. After the tripping fault occurs, the ability to quickly and effectively identify the fault type is crucial to the safe and stable operation of the distribution network in the event of a fault. [0003] For the processing and analysis of a large number of distribution network fault data, artificial intelligence methods can usually be used, such as the use of artificial neural networks, expert systems, fuzzy systems, etc. The artificial neural network has certain advantages due to its sim...

AI Technical Summary

Problems solved by technology

[0006] This application provides a distribution network fault identification method and related devices, which are used to solve the technical problem of poor identification speed and accuracy of distribution network faults in the prior art

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