Open AccessPower Quality Disturbance Identification and Optimization Based on Machine Learning
Author(s) -
Fei Long,
Fen Liu,
Xiangli Peng,
Zheng Yu,
Huan Xu,
Jing Li
Publication year - 2021
Publication title -
distributed generation and alternative energy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.19
H-Index - 12
eISSN - 2156-3306
pISSN - 2156-6550
DOI - 10.13052/dgaej2156-3306.3723
Subject(s) - artificial neural network , computer science , identification (biology) , artificial intelligence , machine learning , set (abstract data type) , noise (video) , disturbance (geology) , quality (philosophy) , construct (python library) , paleontology , philosophy , botany , epistemology , image (mathematics) , biology , programming language
In order to improve the electrical quality disturbance recognition ability of theneural network, this paper studies a depth learning-based power quality dis-turbance recognition and classification method: constructing a power qualityperturbation model, generating training set; construct depth neural network;profit training set to depth neural network training; verify the performance ofthe depth neural network; the results show that the training set is randomlyadded 20DB-50DB noise, even in the most serious 20dB noise conditions,it can reach more than 99% identification, this is a tradition. The methodis impossible to implement. Conclusion: the deepest learning-based powerquality disturbance identification and classification method overcomes thedisadvantage of the selection steps of artificial characteristics, poor robust-ness, which is beneficial to more accurately and quickly discover the categoryof power quality issues.