{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:05:39Z","timestamp":1777705539088,"version":"3.51.4"},"reference-count":13,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,1,25]]},"abstract":"<jats:p>This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), artificial neural network (ANN) and J48 algorithm of machine learning for real-time harmonics analysis of digital substation\u2019s equipment based on IEC-61850 using explanatory input variables based on laboratory proto-type real-time recorded database. In the proposed hybrid model, these variables are first extracted then diagnostic of power transformer harmonics of digital substation is evaluated\/analyzed to perform the long term as well as the short term goal and planning in the electrical power network. In this paper, firstly, experimental analysis is performed to validate the laboratory prototype setup using FFT (fast Fourier transform), STFT (short-time Fourier transform) and CWT (continuous wavelet transform). Then, features are extracted from experimental dataset using EMD (empirical mode decomposition) method. The IMFs (intrinsic mode functions) have generated from EMD, which are used as an input variable to the two different diagnostic models, i.e., ANN and J48 algorithm. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using ANN and J48 method (with and without EMD method) and the results are compared. Obtained results shows that the proposed hybrid diagnostics approach for harmonics analysis has outperformance characteristics.<\/jats:p>","DOI":"10.3233\/jifs-189745","type":"journal-article","created":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T12:12:33Z","timestamp":1613736753000},"page":"741-754","source":"Crossref","is-referenced-by-count":13,"title":["Real-time harmonics analysis of digital substation equipment based on IEC-61850 using hybrid intelligent approach"],"prefix":"10.1177","volume":"42","author":[{"given":"Abdul","family":"Azeem","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Jamia Millia Islamia-New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hasmat","family":"Malik","sequence":"additional","affiliation":[{"name":"BEARS, University Town, NUS Campus, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Majid","family":"Jamil","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Jamia Millia Islamia-New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"6","key":"10.3233\/JIFS-189745_ref3","doi-asserted-by":"publisher","first-page":"4556","DOI":"10.1109\/TIA.2016.2598677","article-title":"Application of Gene Expression Programming (GEP) in Power Transformers Fault Diagnosis Using DGA","volume":"52","author":"Malik","year":"2016","journal-title":"IEEE Transactions on Industry Applications"},{"key":"10.3233\/JIFS-189745_ref5","unstructured":"MATLAB. version 7.10.0 (R2010a). 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Series A: Mathematical, Physical and Engineering Sciences"},{"issue":"4","key":"10.3233\/JIFS-189745_ref8","doi-asserted-by":"publisher","first-page":"3043","DOI":"10.3233\/JIFS-169247","article-title":"EMD and ANN based Intelligent Fault Diagnosis Model for Transmission Line","volume":"32","author":"Malik","year":"2017","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"issue":"5","key":"10.3233\/JIFS-189745_ref9","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.3233\/JIFS-169821","article-title":"EMD and ANN Based Intelligent Model for Bearing Fault Diagnosis","volume":"35","author":"Shah","year":"2018","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.3233\/JIFS-189745_ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICPE-ICES.2016.7853709"},{"key":"10.3233\/JIFS-189745_ref11","first-page":"309","article-title":"Feature Extraction Using EMD and Classifier through Artificial Neural Networks for Gearbox Fault Diagnosis","volume":"697","author":"Pandya","year":"2018","journal-title":"Book chapter in Applications of Artificial Intelligence Techniques in Engineering, Advances in Intelligent Systems and Computing"},{"issue":"6","key":"10.3233\/JIFS-189745_ref12","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1049\/iet-rpg.2015.0382","article-title":"Artificial Neural Network and Empirical Mode Decomposition Based Imbalance Fault Diagnosis of Wind Turbine Using TurbSim, FAST and Simulink","volume":"11","author":"Malik","year":"2017","journal-title":"IET Renewable Power Generation"},{"issue":"16","key":"10.3233\/JIFS-189745_ref13","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.1080\/15325008.2014.956952","article-title":"Selection of Most Relevant Input Parameters Using Waikato Environment for Knowledge Analysis for Gene Expression Programming Based Power Transformer Fault Diagnosis","volume":"42","author":"Malik","year":"2014","journal-title":"International Journal of Electric Power Components and Systems"},{"issue":"12","key":"10.3233\/JIFS-189745_ref14","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1080\/15325008.2017.1338794","article-title":"Selection of Most Relevant Input Parameters Using Principle Component Analysis for Extreme Learning Machine Based Power Transformer Fault Diagnosis Model","volume":"45","author":"Malik","year":"2017","journal-title":"International Journal of Electric Power Components and Systems"},{"key":"10.3233\/JIFS-189745_ref15","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rser.2013.12.008","article-title":"Selection of Most Relevant Input Parameters Using WEKA for Artificial Neural Network Based Solar Radiation Prediction Models","volume":"31","author":"Yadav","year":"2014","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"10.3233\/JIFS-189745_ref16","doi-asserted-by":"publisher","DOI":"10.1109\/POW-ERI.2016.8077368"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-189745","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:44:10Z","timestamp":1777455850000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-189745"}},"subtitle":[],"editor":[{"given":"Hasmat","family":"Malik","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]},{"given":"Gopal","family":"Chaudhary","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]},{"given":"Smriti","family":"Srivastava","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,1,25]]},"references-count":13,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jifs-189745","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,25]]}}}