Interaction of neural network and dung beetle optimizer for characterization of large power transformers considering through-fault protection capability

IF 4.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research Pub Date : 2026-06-01 Epub Date: 2026-02-09 DOI:10.1016/j.epsr.2026.112808

Abdelmonem Draz , Hossam Ashraf , Ahmed R. El Shamy , Attia A. El‑Fergany

{"title":"Interaction of neural network and dung beetle optimizer for characterization of large power transformers considering through-fault protection capability","authors":"Abdelmonem Draz , Hossam Ashraf , Ahmed R. El Shamy , Attia A. El‑Fergany","doi":"10.1016/j.epsr.2026.112808","DOIUrl":null,"url":null,"abstract":"<div><div>Several computational methods aim to accurately identify unknown parameters of transformers (XFMRs) through appropriate objective functions, but there is a need for more accurate models for large power XFMRs in both normal and faulty conditions. This paper presents a novel approach for estimating unknown parameters of power XFMRs by combining metaheuristic optimization with artificial neural networks (ANN). Six unidentified parameters are determined using the dung beetle optimizer (DBO), whose effectiveness is validated against various challenging optimizers. The algorithms capture the steady-state fingerprints of test cases regarding voltage regulation and efficiency characteristics under varying loading conditions. Moreover, ANN is employed to assess XFMR operations in malfunctioning scenarios by predicting protection through-fault capabilities. This includes analyzing frequent fault characteristics due to mechanical damage and infrequent electrical degradation faults via actual measurements of XFMR parameters. The proposed method is applied to a real-case power XFMR of 40 MVA and a benchmark 4 kVA XFMR, confirming that frequent fault characteristics for the 40 MVA XFMR begin at 4.06 times the rated current. The findings illustrate the synergistic capabilities of ANN and DBO in characterizing key parameters and performance of power XFMRs.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112808"},"PeriodicalIF":4.3000,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electric Power Systems Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S037877962600101X","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/2/9 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 0

Abstract

Several computational methods aim to accurately identify unknown parameters of transformers (XFMRs) through appropriate objective functions, but there is a need for more accurate models for large power XFMRs in both normal and faulty conditions. This paper presents a novel approach for estimating unknown parameters of power XFMRs by combining metaheuristic optimization with artificial neural networks (ANN). Six unidentified parameters are determined using the dung beetle optimizer (DBO), whose effectiveness is validated against various challenging optimizers. The algorithms capture the steady-state fingerprints of test cases regarding voltage regulation and efficiency characteristics under varying loading conditions. Moreover, ANN is employed to assess XFMR operations in malfunctioning scenarios by predicting protection through-fault capabilities. This includes analyzing frequent fault characteristics due to mechanical damage and infrequent electrical degradation faults via actual measurements of XFMR parameters. The proposed method is applied to a real-case power XFMR of 40 MVA and a benchmark 4 kVA XFMR, confirming that frequent fault characteristics for the 40 MVA XFMR begin at 4.06 times the rated current. The findings illustrate the synergistic capabilities of ANN and DBO in characterizing key parameters and performance of power XFMRs.

查看原文本刊更多论文

考虑过故障保护能力的大型电力变压器神经网络与屎壳虫优化器的交互作用

目前已有一些计算方法旨在通过适当的目标函数准确识别变压器的未知参数，但对于大功率XFMRs，无论是在正常状态还是故障状态下，都需要更精确的模型。提出了一种将元启发式优化与人工神经网络相结合的功率xfmr未知参数估计方法。使用蜣螂优化器（DBO）确定了六个未确定的参数，其有效性针对各种具有挑战性的优化器进行了验证。该算法捕获了不同负载条件下电压调节和效率特性测试用例的稳态指纹。此外，通过预测故障保护能力，采用人工神经网络来评估故障场景下的XFMR操作。这包括通过实际测量XFMR参数来分析由于机械损伤引起的频繁故障特征和不常见的电气退化故障。将该方法应用于40 MVA的实际功率XFMR和4 kVA的基准XFMR，证实了40 MVA XFMR的频繁故障特征始于额定电流的4.06倍。研究结果说明了人工神经网络和DBO在表征功率xfmr的关键参数和性能方面的协同能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Electric Power Systems Research 工程技术-工程：电子与电气

CiteScore

7.50

自引率

17.90%

发文量

963

审稿时长

3.8 months

期刊介绍： Electric Power Systems Research is an international medium for the publication of original papers concerned with the generation, transmission, distribution and utilization of electrical energy. The journal aims at presenting important results of work in this field, whether in the form of applied research, development of new procedures or components, orginal application of existing knowledge or new designapproaches. The scope of Electric Power Systems Research is broad, encompassing all aspects of electric power systems. The following list of topics is not intended to be exhaustive, but rather to indicate topics that fall within the journal purview. • Generation techniques ranging from advances in conventional electromechanical methods, through nuclear power generation, to renewable energy generation. • Transmission, spanning the broad area from UHV (ac and dc) to network operation and protection, line routing and design. • Substation work: equipment design, protection and control systems. • Distribution techniques, equipment development, and smart grids. • The utilization area from energy efficiency to distributed load levelling techniques. • Systems studies including control techniques, planning, optimization methods, stability, security assessment and insulation coordination.