Method for monitoring transformer's fault state based on Spiking neural network

Abstract

The invention provides a method for monitoring transformer's fault state based on Spiking neural network. The method comprises the following steps: using the characteristic gas method to determine the number of neural elements in the input layer and the output layer of the Spiking neural network and to determine the input sample data; determining the number of neural elements in the hidden layer of the Spiking neural network; normalizing the input sample data; coding the input sample data; and inputting the input sample data to the Spiking neural network to have the data trained; outputting the trained data as the monitoring result for the transformer's fault state. In view of the monitoring problem with the transformer's fault state, the method of the invention uses an oil-filled power transformer as a study object. And the Spiking neural network adopted by the method employs the coding mode of accurate pulse time, which is closer to the real biological neural system and enables it to have a stronger computing ability than other neural networks so as to effectively identify the type of fault, reduce erroneous determination and the erroneous determination rate and provide accuracy for diagnosis.

Description

technical field [0001] The invention belongs to the technical field of transformer fault state monitoring, in particular to a transformer fault state monitoring method based on a Spiking neural network. Background technique [0002] The power transformer is the key equipment for energy transmission in the power network, and its working status will directly affect the load distribution and power supply reliability of the entire power grid. The use of fault diagnosis methods can detect internal early faults in time, evaluate the status of transformers, provide decision-making reference for load scheduling, formulate condition-based maintenance plans, and ensure reliable and safe operation of the power grid. The insulating oil and organic insulating materials in the transformer will gradually age and decompose under the long-term action of heat and electricity with the increase of operating time, and produce a very small amount of gas. The characteristic gases dissolved in thes...

AI Technical Summary

Problems solved by technology

Although some scholars have proposed some intelligent diagnostic methods based on BP, RBF neural network theory, gray system theory, expert system theory, fuzzy mathematics theory, etc., these diagnostic methods still face huge challenges in dealing with a large amount of historical data and fault monitoring accuracy.

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