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Erschienen in:
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2023 | OriginalPaper | Buchkapitel

Feature Fusion Based IPSO-LSSVM Fault Diagnosis of On-Load Tap Changers

verfasst von : Honghua Xu, Laibi Yin, Ziqiang Xu, Qian Xin, Mengjie Lv

Erschienen in: Big Data and Security

Verlag: Springer Nature Singapore

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Abstract

Aiming at the problem of single characteristic quantity and low accuracy of traditional on-load tap changers, this paper presents a fault diagnosis algorithm of on-load tap changers based on IPSO-LSSVM. Firstly, the time-frequency signal extracted from the vibration signal and the improved scale permutation entropy form a multi-feature fusion matrix; Then, considering the redundancy and high computational complexity of the multi-dimensional feature matrix, the principal component analysis is used to screen the feature subset and obtain the sensitive feature subset; Finally, IPSO-LSSVM is used to select and classify the initial feature subset. Experimental results show that the classification accuracy of IPSO-LSSVM is improved by at least 5% compared with other methods.

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Metadaten
Titel
Feature Fusion Based IPSO-LSSVM Fault Diagnosis of On-Load Tap Changers
verfasst von
Honghua Xu
Laibi Yin
Ziqiang Xu
Qian Xin
Mengjie Lv
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
Buch
Big Data and Security

Print ISBN: 978-981-9932-99-3

Electronic ISBN: 978-981-9933-00-6

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-981-99-3300-6_14

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