Improved GWO-MCSVM algorithm based on nonlinear convergence factor and tent chaotic mapping and its application in transformer condition assessment-Reference-Cited by-同舟云学术

Improved GWO-MCSVM algorithm based on nonlinear convergence factor and tent chaotic mapping and its application in transformer condition assessment

Published:2023-11 Issue: Volume:224 Page:109754
ISSN:0378-7796
Container-title:Electric Power Systems Research
language:en
Short-container-title:Electric Power Systems Research

Author:

ZHANG Qizhao,LIU Hongshun^ORCID,GUO Jian,WANG Yifan,LIU Luyao,LIU Hongzheng,CONG Haoxi

Funder

North China Electric Power University

Natural Science Foundation of Shandong Province

State Key Laboratory Of Alternate Electrical Power System With Renewable Energy Sources

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference34 articles.

1. Power transformer health condition assessment: a deep generative model aided intelligent framework;Naimul;Electr. Power Syst. Res.,2023

2. Fault detection and diagnosis in power transformers: a comprehensive review and classification of publications and methods;Reza;Electr. Power Syst. Res.,2022

3. Power transformer fault prediction and health management: challenges and Outlook;Gang;Power Syst. Automation,2017

4. Incipient fault diagnosis in power transformers by data-driven models with over-sampled dataset;Andrade;Electr. Power Syst. Res.,2021

5. Optimal exploitation of power transformer fleet considering loss of life and economic assessment based on failure probability;Mohsen;Electr. Power Syst. Res.,2022

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