000096073 001__ 96073 000096073 005__ 20231116120808.0 000096073 0247_ $$2doi$$a10.24084/repqj18.317 000096073 0248_ $$2sideral$$a120417 000096073 037__ $$aART-2020-120417 000096073 041__ $$aeng 000096073 100__ $$aDomínguez Gimeno, Sergio 000096073 245__ $$aAnalysis of public datasets of power quality distortions 000096073 260__ $$c2020 000096073 5060_ $$aAccess copy available to the general public$$fUnrestricted 000096073 5203_ $$aAutomatic classification of power quality distortions has gained interest in research due to the proliferation of distributed power systems with renewable sources. To train and test a classification system, data with power quality distortions are required. Most studies generate synthetic data from mathematical equations, since real distortions are difficult to record. A possible alternative is to use public datasets of real disturbances. However, there are strong differences among public datasets. In this paper, existing datasets of power quality distortions were compiled and their main features were analysed and compared. To the best of our knowledge, this is the first work reviewing these datasets. To identify the datasets, the most cited papers on this topic were surveyed. In addition, systematic searches were conducted in four popular scientific repositories. As a result, four available datasets were identified. They included a limited number of samples (20- 44) and types of distortions. Sampling frequencies and recording conditions were appropriate and the two main fundamental grid frequencies (50 and 60 Hz) were also considered. Although these datasets are appropriate for partially testing automatic classifiers, a remaining research effort is to provide comprehensive datasets with hundreds of samples and several types of distortions. 000096073 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/T49-17R$$9info:eu-repo/grantAgreement/ES/MECD/CAS18/218$$9info:eu-repo/grantAgreement/ES/UZ/IT1-19 000096073 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000096073 592__ $$a0.136$$b2020 000096073 593__ $$aElectrical and Electronic Engineering$$c2020$$dQ4 000096073 593__ $$aRenewable Energy, Sustainability and the Environment$$c2020$$dQ4 000096073 593__ $$aEnergy Engineering and Power Technology$$c2020$$dQ4 000096073 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000096073 700__ $$0(orcid)0000-0002-1561-0536$$aIgual Catalán, Raúl$$uUniversidad de Zaragoza 000096073 700__ $$0(orcid)0000-0001-7671-7540$$aMedrano Sánchez, Carlos$$uUniversidad de Zaragoza 000096073 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica 000096073 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica 000096073 773__ $$g18 (2020), 321-326$$pRenewable energy power qual. j.$$tRenewable Energy and Power Quality Journal$$x2172-038X 000096073 8564_ $$s1321585$$uhttps://zaguan.unizar.es/record/96073/files/texto_completo.pdf$$yVersión publicada 000096073 8564_ $$s622729$$uhttps://zaguan.unizar.es/record/96073/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000096073 909CO $$ooai:zaguan.unizar.es:96073$$particulos$$pdriver 000096073 951__ $$a2023-11-16-12:00:12 000096073 980__ $$aARTICLE