Power Quality Disturbance Identification and Optimization Based on Machine Learning
DOI:
https://doi.org/10.13052/dgaej2156-3306.3723Keywords:
Power quality disturbance, deep learning, convolutional neural network.Abstract
In order to improve the electrical quality disturbance recognition ability of the
neural network, this paper studies a depth learning-based power quality dis-
turbance recognition and classification method: constructing a power quality
perturbation model, generating training set; construct depth neural network;
profit training set to depth neural network training; verify the performance of
the depth neural network; the results show that the training set is randomly
added 20DB-50DB noise, even in the most serious 20dB noise conditions,
it can reach more than 99% identification, this is a tradition. The method
is impossible to implement. Conclusion: the deepest learning-based power
quality disturbance identification and classification method overcomes the
disadvantage of the selection steps of artificial characteristics, poor robust-
ness, which is beneficial to more accurately and quickly discover the category
of power quality issues.
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