Research on transformer fault diagnosis based on genetic algorithm optimized neural network

Autor:	Lei Zhang, Xianwen Zeng
Rok vydání:	2021
Předmět:	History Artificial neural network Computer science Genetic algorithm ComputingMethodologies_GENERAL Data mining Fault (power engineering) computer.software_genre computer Computer Science Applications Education Transformer (machine learning model)
Zdroj:	Journal of Physics: Conference Series. 1848:012004
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1848/1/012004
Popis:	Aiming at the problem of transformer fault diagnosis in power system, a fault diagnosis method based on genetic algorithm optimization BP neural network is proposed. The BP neural network structure was established based on the traditional three-ratio fault diagnosis method. The concentration of dissolved gas in transformer oil was taken as the input value of the neural network, and the fault working state of transformer was taken as the output value. The established neural network is used to diagnose transformer faults. In order to avoid BP neural network falling into the problem of local optimal value, the weights and thresholds of the neural network are optimized by genetic algorithm. By using the genetic algorithm toolbox in Matlab to establish the genetic algorithm network structure, the transformer fault data as input for the performance of the neural network test. The results show that the neural network optimized by genetic algorithm has a high classification effect on transformer fault types, and the neural network optimized by genetic algorithm has a higher diagnostic efficiency and accuracy than BP neural network.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7b10251bb35dc64d59255e6b1daace91 https://doi.org/10.1088/1742-6596/1848/1/012004 Zobrazit plný text záznamu