000096193 001__ 96193 000096193 005__ 20210902121901.0 000096193 0247_ $$2doi$$a10.24084/repqj18.248 000096193 0248_ $$2sideral$$a120592 000096193 037__ $$aART-2020-120592 000096193 041__ $$aeng 000096193 100__ $$0(orcid)0000-0001-9823-4777$$aTalayero, A.P. 000096193 245__ $$aDiagnosis of failures in solar plants based on performance monitoring 000096193 260__ $$c2020 000096193 5060_ $$aAccess copy available to the general public$$fUnrestricted 000096193 5203_ $$aPhotovoltaic (PV) solar energy has become a reference in electrical generation. The plants currently installed, and those planned have a huge capacity and occupy large areas. The increase in size of the plants presents new challenges in operation and maintenance areas, such as the optimization of the number of sensors installed, large data management and the reduction of the timework in maintenance. The aim of this paper is to show a methodology, to diagnose failures, based on the measured data in the plant. The methodology used is supervised regression machine learning and comparison algorithms. This methodology allows the study of the sensors, the inverters, the joint boxes and the power reduction caused by soiling. The result would allow the detection of around 1-5% of production loss in the plant. The algorithms have been tested with real data of PV plants, and have detected common failures such as production drops in strings and losses due to soiling. 000096193 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000096193 592__ $$a0.136$$b2020 000096193 593__ $$aElectrical and Electronic Engineering$$c2020$$dQ4 000096193 593__ $$aRenewable Energy, Sustainability and the Environment$$c2020$$dQ4 000096193 593__ $$aEnergy Engineering and Power Technology$$c2020$$dQ4 000096193 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000096193 700__ $$0(orcid)0000-0001-6350-4474$$aLlombart, A. 000096193 700__ $$0(orcid)0000-0003-2360-0845$$aMelero, J.J.$$uUniversidad de Zaragoza 000096193 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica 000096193 773__ $$g18 (2020), 128-133$$pRenewable energy power qual. j.$$tRenewable Energy and Power Quality Journal$$x2172-038X 000096193 8564_ $$s277665$$uhttps://zaguan.unizar.es/record/96193/files/texto_completo.pdf$$yVersión publicada 000096193 8564_ $$s560935$$uhttps://zaguan.unizar.es/record/96193/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000096193 909CO $$ooai:zaguan.unizar.es:96193$$particulos$$pdriver 000096193 951__ $$a2021-09-02-10:35:53 000096193 980__ $$aARTICLE