基于老化参数的变压器相对老化率预测

IF 4.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Array Pub Date : 2025-06-14 DOI:10.1016/j.array.2025.100433
Mohsen Hosseinkhanloo , Navid Taghizadegan Kalantari , Vahid Behjat , Sajad Najafi Ravadanegh
{"title":"基于老化参数的变压器相对老化率预测","authors":"Mohsen Hosseinkhanloo ,&nbsp;Navid Taghizadegan Kalantari ,&nbsp;Vahid Behjat ,&nbsp;Sajad Najafi Ravadanegh","doi":"10.1016/j.array.2025.100433","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents a novel quantitative approach utilizing machine learning (ML) techniques to predict the aging rate of power transformers based on key aging factors, including loading level, temperature, moisture, and oxygen. Traditional methods for calculating aging rates are limited to discrete values of aging factors, which restricts their applicability in real-world scenarios. Proposed method extends the calculation of transformer aging rate using ML by training data which is achieved by the discrete and limited values obtained in experimental works in laboratories. For this purpose, ML models including Gaussian Process Regression (GPR), Support Vector Machines (SVM), Neural Networks (NN), Fine Tree (FT), Linear Regression (LR), Kernel and Ensemble are utilized to expand aging rate calculation. Values of metrics indicate that GPR had the best accuracy (RMSE = 0.055, R<sup>2</sup> = 1, MSE = 0.003, MAE = 0.028 and MAPE = 7 %). On the contrary, LR had the worst accuracy considering the values RMSE = 3.462, R<sup>2</sup> = 0.57, MSE = 11.986, MAE = 2.36 and MAPE = 2196 %. Taking prediction speed into account, NN had the higher value of 5800 (obs/sec), while GPR had the value of 2600 (obs/sec). Moreover, training time was lower in FT and LR (30.3 s and 34.8 s respectively) compared to other models with higher accuracy.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100433"},"PeriodicalIF":4.5000,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Utilizing machine learning for transformer relative aging rate prediction based on aging parameters\",\"authors\":\"Mohsen Hosseinkhanloo ,&nbsp;Navid Taghizadegan Kalantari ,&nbsp;Vahid Behjat ,&nbsp;Sajad Najafi Ravadanegh\",\"doi\":\"10.1016/j.array.2025.100433\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper presents a novel quantitative approach utilizing machine learning (ML) techniques to predict the aging rate of power transformers based on key aging factors, including loading level, temperature, moisture, and oxygen. Traditional methods for calculating aging rates are limited to discrete values of aging factors, which restricts their applicability in real-world scenarios. Proposed method extends the calculation of transformer aging rate using ML by training data which is achieved by the discrete and limited values obtained in experimental works in laboratories. For this purpose, ML models including Gaussian Process Regression (GPR), Support Vector Machines (SVM), Neural Networks (NN), Fine Tree (FT), Linear Regression (LR), Kernel and Ensemble are utilized to expand aging rate calculation. Values of metrics indicate that GPR had the best accuracy (RMSE = 0.055, R<sup>2</sup> = 1, MSE = 0.003, MAE = 0.028 and MAPE = 7 %). On the contrary, LR had the worst accuracy considering the values RMSE = 3.462, R<sup>2</sup> = 0.57, MSE = 11.986, MAE = 2.36 and MAPE = 2196 %. Taking prediction speed into account, NN had the higher value of 5800 (obs/sec), while GPR had the value of 2600 (obs/sec). Moreover, training time was lower in FT and LR (30.3 s and 34.8 s respectively) compared to other models with higher accuracy.</div></div>\",\"PeriodicalId\":8417,\"journal\":{\"name\":\"Array\",\"volume\":\"27 \",\"pages\":\"Article 100433\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Array\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590005625000608\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Array","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590005625000608","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0

摘要

本文提出了一种新的定量方法,利用机器学习(ML)技术来预测电力变压器的老化率,该方法基于关键老化因素,包括负载水平、温度、湿度和氧气。传统的老化率计算方法仅限于老化因子的离散值,限制了其在实际应用中的适用性。本文提出的方法扩展了用机器学习计算变压器老化率的方法,该方法利用实验室实验中获得的离散值和有限值来获得训练数据。为此,ML模型包括高斯过程回归(GPR)、支持向量机(SVM)、神经网络(NN)、细树(FT)、线性回归(LR)、核和集成(Kernel and Ensemble)来扩展老化率计算。指标值表明,GPR的准确率最高(RMSE = 0.055, R2 = 1, MSE = 0.003, MAE = 0.028, MAPE = 7%)。考虑到RMSE = 3.462, R2 = 0.57, MSE = 11.986, MAE = 2.36, MAPE = 2196 %, LR的准确率最差。考虑到预测速度,NN的预测速度较高,为5800 (obs/sec),而GPR的预测速度为2600 (obs/sec)。与其他准确率较高的模型相比,FT和LR的训练时间较短(分别为30.3 s和34.8 s)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilizing machine learning for transformer relative aging rate prediction based on aging parameters
This paper presents a novel quantitative approach utilizing machine learning (ML) techniques to predict the aging rate of power transformers based on key aging factors, including loading level, temperature, moisture, and oxygen. Traditional methods for calculating aging rates are limited to discrete values of aging factors, which restricts their applicability in real-world scenarios. Proposed method extends the calculation of transformer aging rate using ML by training data which is achieved by the discrete and limited values obtained in experimental works in laboratories. For this purpose, ML models including Gaussian Process Regression (GPR), Support Vector Machines (SVM), Neural Networks (NN), Fine Tree (FT), Linear Regression (LR), Kernel and Ensemble are utilized to expand aging rate calculation. Values of metrics indicate that GPR had the best accuracy (RMSE = 0.055, R2 = 1, MSE = 0.003, MAE = 0.028 and MAPE = 7 %). On the contrary, LR had the worst accuracy considering the values RMSE = 3.462, R2 = 0.57, MSE = 11.986, MAE = 2.36 and MAPE = 2196 %. Taking prediction speed into account, NN had the higher value of 5800 (obs/sec), while GPR had the value of 2600 (obs/sec). Moreover, training time was lower in FT and LR (30.3 s and 34.8 s respectively) compared to other models with higher accuracy.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信