{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:56:27Z","timestamp":1754157387885,"version":"3.41.2"},"reference-count":12,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2008,9,12]],"date-time":"2008-09-12T00:00:00Z","timestamp":1221177600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,9,12]]},"abstract":"Purpose<\/jats:title>The purpose of this paper is to develop a new method for detection and classification of power quality disturbances such as transients, waveform distortions, sags, swells and interruptions.<\/jats:p><\/jats:sec>Design\/methodology\/approach<\/jats:title>For the purposes of the proposed method, the power quality disturbances are divided into two groups. Different algorithms are applied to detect and classify the disturbances from each of the two groups. For the processing of transients and waveform distortions, digital high\u2010pass filter and the mathematical morphology closing are used. Calculation of the RMS value is used for detection of sags, swells and interruptions.<\/jats:p><\/jats:sec>Findings<\/jats:title>The proposed method was implemented in a PC\u2010based measuring setup. The measuring setup was used in a seven\u2010months\u2010long monitoring of a single\u2010phase power system. In the course of the monitoring, the proposed method was verified on over 19,000 transients, 3,500 waveform distortions, 77 sags and 18 interruptions.<\/jats:p><\/jats:sec>Research limitations\/implications<\/jats:title>The classification stage of the proposed method does not differentiate between individual types of waveform distortions (harmonics, interharmonics, noise\u2026).<\/jats:p><\/jats:sec>Practical implications<\/jats:title>The described approach is simpler and more reliable than, for example, methods based solely on wavelet transform. The proposed method is suitable for real\u2010time monitoring of power systems.<\/jats:p><\/jats:sec>Originality\/value<\/jats:title>The paper describes a new and efficient way of detection and classification of disturbances (especially of transients and waveform distortions). It shows that mathematical morphology operations, which are normally used in image processing, represent a useful tool also in the field of power quality measurements.<\/jats:p><\/jats:sec>","DOI":"10.1108\/03321640810890843","type":"journal-article","created":{"date-parts":[[2008,9,27]],"date-time":"2008-09-27T07:05:18Z","timestamp":1222499118000},"page":"1178-1191","source":"Crossref","is-referenced-by-count":6,"title":["An efficient approach to detect and classify power quality disturbances"],"prefix":"10.1108","volume":"27","author":[{"given":"Tom\u00e1\u0161","family":"Radil","sequence":"first","affiliation":[]},{"given":"Fernando M.","family":"Janeiro","sequence":"additional","affiliation":[]},{"given":"Pedro M.","family":"Ramos","sequence":"additional","affiliation":[]},{"given":"A.","family":"Cruz Serra","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022031220044511600_b1","unstructured":"Burns, C.S., Gopinath, R.A. and Guo, H. (1998), Introduction to Wavelets and Wavelet Transforms: A Primer, Prentice\u2010Hall, Englewood Cliffs, NJ."},{"key":"key2022031220044511600_b2","unstructured":"Chen, Z. and Urwin, P. (2001), \u201cPower quality detection and classification using digital filters\u201d, IEEE Power Tech 2001, Porto, Vol. 1."},{"key":"key2022031220044511600_b3","doi-asserted-by":"crossref","unstructured":"Gaing, Z\u2010L. (2004), \u201cWavelet\u2010based neural network for power disturbance recognition and classification\u201d, IEEE Trans. Power Del., Vol. 19 No. 4.","DOI":"10.1109\/TPWRD.2004.835281"},{"key":"key2022031220044511600_b4","doi-asserted-by":"crossref","unstructured":"Gaouda, A.M., Salama, M.M.A., Sultan, M.R. and Chikhani, A.Y. (1999), \u201cPower quality detection and classification using wavelet\u2010multiresolution signal decomposition\u201d, IEEE Trans. Power Del., Vol. 14 No. 4, pp. 1469\u201076.","DOI":"10.1109\/61.796242"},{"key":"key2022031220044511600_b5","doi-asserted-by":"crossref","unstructured":"He, H. and Starzyk, J.A. (2006), \u201cA self\u2010organizing learning array system for power quality classification based on wavelet transform\u201d, IEEE Trans. Power Del., Vol. 21 No. 1, pp. 286\u201095.","DOI":"10.1109\/TPWRD.2005.852392"},{"key":"key2022031220044511600_b6","unstructured":"Hu, G., Xie, J. and Zhu, F. (2004), \u201cClassification of power quality disturbances using wavelet and support vector machines\u201d, IEEE Trans. Power Del., Vol. 19 No. 4."},{"key":"key2022031220044511600_b7","unstructured":"IEEE Std 1159\u20101995 (1995), IEEE Recommended Practice for Monitoring Electric Power Quality, The Institute of Electrical and Electronics Engineers, Inc., New York, NY."},{"key":"key2022031220044511600_b8","doi-asserted-by":"crossref","unstructured":"Matz, V., Radil, T., Ramos, P.M. and Serra, A.C. (2007), \u201cAutomated power quality monitoring system for on\u2010line detection and classification of disturbances\u201d, paper presented at IEEE Instrumentation and Measurement Technology Conference, Warsaw.","DOI":"10.1109\/IMTC.2007.379104"},{"key":"key2022031220044511600_b9","doi-asserted-by":"crossref","unstructured":"Poisson, O., Rioual, P. and Meunier, M. (2000), \u201cDetection and measurement of power quality disturbances using wavelet transform\u201d, IEEE Trans. Power Del., Vol. 15 No. 3, pp. 1039\u201044.","DOI":"10.1109\/61.871372"},{"key":"key2022031220044511600_b10","doi-asserted-by":"crossref","unstructured":"Santoso, S., Powers, E.J., Grady, W.M. and Hofmann, P. (1996), \u201cPower quality assessment via wavelet transform analysis\u201d, IEEE Trans. Power Del., Vol. 11 No. 2, pp. 924\u201030.","DOI":"10.1109\/61.489353"},{"key":"key2022031220044511600_b11","unstructured":"Serra, J. (1982), Image Analysis and Mathematical Morphology, Volume 1, Academic Press, New York, NY."},{"key":"key2022031220044511600_b12","doi-asserted-by":"crossref","unstructured":"Styvaktakis, E., Bollen, M.H.J. and Gu, I.Y.H. (2002), \u201cAutomatic classification of power system events using RMS voltage measurements\u201d, IEEE Power Engineering Society Summer Meeting, Vol. 2, pp. 824\u20109.","DOI":"10.1109\/PESS.2002.1043446"}],"container-title":["COMPEL - The international journal for computation and mathematics in electrical and electronic engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.emeraldinsight.com\/doi\/full-xml\/10.1108\/03321640810890843","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/03321640810890843\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/03321640810890843\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T23:48:50Z","timestamp":1753400930000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/compel\/article\/27\/5\/1178-1191\/101896"}},"subtitle":[],"editor":[{"given":"Ahmed","family":"Masmoudi","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2008,9,12]]},"references-count":12,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2008,9,12]]}},"alternative-id":["10.1108\/03321640810890843"],"URL":"https:\/\/doi.org\/10.1108\/03321640810890843","relation":{},"ISSN":["0332-1649"],"issn-type":[{"type":"print","value":"0332-1649"}],"subject":[],"published":{"date-parts":[[2008,9,12]]}}}