In:
IOP Conference Series: Earth and Environmental Science, IOP Publishing, Vol. 781, No. 4 ( 2021-05-01), p. 042047-
Abstract:
Oil chromatographic analysis (DGA) is an important way to transformer fault diagnosis, combining research topics based on I-K-means clustering, t SNE visual clustering in data mining, fault classification number. In order to improve the convergence speed of neural network, an improved back-propagation BP neural network using ADAM gradient optimization algorithm instead of traditional stochastic gradient descent optimization to update the weight of neural network is proposed. Fuzzy C-means clustering and particle swarm optimization are proposed to optimize the initial parameters of neural network. By using 3500 data samples of transformers from a power plant in a city of Liaoning Province to carry out simulation experiments, and comparing the traditional BP network algorithm, CPSO-BP network algorithm and the CPSO-BP network algorithm optimized by Adam, it is proved that the CPSO-BP network optimized by Adam has fast training convergence, strong generalization ability and high accuracy. At the same time, the accuracy, precision, recall and F-1 values were used to evaluate the CPSO-BP network algorithm optimized by ADAM to verify the effectiveness and stability of the algorithm in transformer fault diagnosis.
Type of Medium:
Online Resource
ISSN:
1755-1307
,
1755-1315
DOI:
10.1088/1755-1315/781/4/042047
Language:
Unknown
Publisher:
IOP Publishing
Publication Date:
2021
detail.hit.zdb_id:
2434538-6
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