{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:05:32Z","timestamp":1777705532760,"version":"3.51.4"},"reference-count":20,"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>In the modern electrical power system network (EPSN), the power quality disturbances (PSDs) are the serious issue for the power engineer to maintain the uninterrupted and reliable power supply. Generally, PQDs are generated due to non-linear loading conditions, perturb loading and other occurrences such as transient, harmonics, sag, swell and interruptions. These problems of PQDs effect the power demand mapping problem, which effect the reliability and stability of the EPSN operating condition. In this study, a novel approach for PQDs diagnosis (PQDD) is proposed, which includes real-time data generation, data pre-processing, feature extraction, feature selection, intelligent model development for PQDD. Data decomposition approach of EMD is utilized to generate the feature vector of IMFs. These features are utilized as an input variables to the intelligent classifiers. In this study, PQDD is analyzed based on SVM method and obtained results are compared with conventional AI method of LVQ-NN. The results represent the higher acceptability of the proposed approach with diagnosis accuracy of 99.98% (training phase), 93.11% (testing phase) for SVM and 92.56% (training phase) and 91.0% (testing phase) for LVQ-NN based PQDD method.<\/jats:p>","DOI":"10.3233\/jifs-189739","type":"journal-article","created":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T12:08:57Z","timestamp":1613736537000},"page":"669-678","source":"Crossref","is-referenced-by-count":15,"title":["Power quality disturbance analysis using data-driven EMD-SVM hybrid approach"],"prefix":"10.1177","volume":"42","author":[{"given":"Hasmat","family":"Malik","sequence":"first","affiliation":[{"name":"BEARS, University Town, NUS Campus, Singapore"}]},{"given":"Abdulaziz","family":"Almutairi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Majmaah University, Al Majma\u2019ah, Saudi Arabia"}]},{"given":"Majed A.","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-189739_ref1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/978-981-13-1822-1_6","article-title":"A Hybrid Intelligent Model for Power Quality Disturbance Classification, Book chapter in Applications of Artificial Intelligence Techniques in Engineering","volume":"697","author":"Malik","year":"2018","journal-title":"Advances in Intelligent Systems and Computing"},{"key":"10.3233\/JIFS-189739_ref2","doi-asserted-by":"crossref","unstructured":"Gaouda A.M. , El-Saadany E.F. , Salama M.M.A. , Sood V.K. and Chikhani A.Y. , Monitoring HVDC Systems Using Wavelet Multi-Resolution Analysis, IEEE Trans on Pow Sys 16(4) (2001).","DOI":"10.1109\/59.962411"},{"key":"10.3233\/JIFS-189739_ref3","doi-asserted-by":"crossref","unstructured":"Chung J. , Edward J. , et al., Power Disturbance Classifier Using a Rule-Based and Wavelet Packet-Based Hidden Markov Model, IEEE Trans On Pow Deli 17(1) (2002).","DOI":"10.1109\/61.974212"},{"key":"10.3233\/JIFS-189739_ref4","doi-asserted-by":"crossref","unstructured":"Dash P.K. , Mishra S. , et al., Classification of Power System Disturbances Using a Fuzzy Expert System and a Fourier Linear Combiner, IEEE Trans on Pow Deli 15(2) (2000).","DOI":"10.1109\/61.852971"},{"key":"10.3233\/JIFS-189739_ref5","doi-asserted-by":"crossref","unstructured":"Ignatova V. , et al., Space Vector method for Voltage dips and swells analysis, IEEE Transactions on Power Delivery 24(4) (2009).","DOI":"10.1109\/TPWRD.2009.2028787"},{"key":"10.3233\/JIFS-189739_ref6","doi-asserted-by":"crossref","unstructured":"Li Z. , et al., A Classification Method for Complex Power Quality Disturbances Using EEMD and Rank Wavelet SVM, IEEE Trans on Indus info 6(4) (2015).","DOI":"10.1109\/TSG.2015.2397431"},{"key":"10.3233\/JIFS-189739_ref7","doi-asserted-by":"crossref","unstructured":"Kumar R. , Singh B. , et al., Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and Rule based Decision Tree, IEEE Trans. on Pow. Deli 51(2) (2015).","DOI":"10.1109\/TIA.2014.2356639"},{"key":"10.3233\/JIFS-189739_ref8","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1016\/j.epsr.2010.07.001","article-title":"Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm","volume":"80","author":"Hooshmand","year":"2010","journal-title":"Electric Power Systems Research"},{"key":"10.3233\/JIFS-189739_ref9","doi-asserted-by":"crossref","unstructured":"He S. , et al., A Real-Time Power Quality disturbances classification using Hybrid Method based on S-Transform and Dynamics, IEEE Transa on Instru And Meas 62(9) (2013).","DOI":"10.1109\/TIM.2013.2258761"},{"key":"10.3233\/JIFS-189739_ref10","doi-asserted-by":"crossref","unstructured":"Li Z. , et al., A Classification Method for Complex Power Quality Disturbances Using EEMD and Rank Wavelet SVM, IEEE Trans on Indus info 6(4) (2015).","DOI":"10.1109\/TSG.2015.2397431"},{"key":"10.3233\/JIFS-189739_ref11","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.ijepes.2012.08.020","article-title":"Classification of disturbances in hybrid DG system using modular PNN and SVM","volume":"44","author":"Mohanty","year":"2013","journal-title":"Electrical Pow and Ene Sys"},{"key":"10.3233\/JIFS-189739_ref12","doi-asserted-by":"crossref","unstructured":"Ray P.K. , et al., Optimal Feature and Decision Tree based classification of Power Quality disturbances in Distributed Generation Systems, IEEE Trans on Sustainable Energy 5(1) (2014).","DOI":"10.1109\/TSTE.2013.2278865"},{"key":"10.3233\/JIFS-189739_ref13","doi-asserted-by":"crossref","unstructured":"Huang J. , et al. Neural-Fuzzy Classifier for Recognition of Power Quality Disturbances, IEEE Trans on Pow Deli 17(2) (2002).","DOI":"10.1109\/61.997947"},{"key":"10.3233\/JIFS-189739_ref14","doi-asserted-by":"crossref","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 and Fuzzy system"},{"issue":"6","key":"10.3233\/JIFS-189739_ref15","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\u201d","volume":"11","author":"Malik","year":"2017","journal-title":"IET Renewable Power Generation"},{"issue":"16","key":"10.3233\/JIFS-189739_ref16","doi-asserted-by":"publisher","first-page":"4041","DOI":"10.1049\/iet-gtd.2017.0331","article-title":"Transmission Line Fault Classification Using Modified Fuzzy Q Learning","volume":"11","author":"Malik","year":"2017","journal-title":"IET Generation Transmission & Distribution"},{"issue":"5","key":"10.3233\/JIFS-189739_ref17","doi-asserted-by":"publisher","first-page":"5123","DOI":"10.3233\/JIFS-169796","article-title":"Decrypting Wrist Movement From MEG Signal Using SVM Classifier","volume":"35","author":"Shahid","year":"2018","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.3233\/JIFS-189739_ref18","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1049\/cp.2015.1679","article-title":"in","volume":"2015","author":"Malik","year":"2015","journal-title":"Michael Faraday IET International Summit"},{"issue":"5","key":"10.3233\/JIFS-189739_ref19","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-189739_ref21","unstructured":"MATLAB and Simulink Toolbox Release 2014b, The MathWorks, Inc., Natick, Massachusetts, United States."}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-189739","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:44:08Z","timestamp":1777455848000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-189739"}},"subtitle":[],"editor":[{"given":"Hasmat","family":"Malik","sequence":"additional","affiliation":[]},{"given":"Gopal","family":"Chaudhary","sequence":"additional","affiliation":[]},{"given":"Smriti","family":"Srivastava","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,1,25]]},"references-count":20,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jifs-189739","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]]}}}