96193 20210902121901.0 doi 10.24084/repqj18.248 sideral 120592 ART-2020-120592 eng Talayero, A.P. (orcid)0000-0001-9823-4777 Diagnosis of failures in solar plants based on performance monitoring 2020 Access copy available to the general public Unrestricted Photovoltaic (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. info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 0.136 2020 Electrical and Electronic Engineering 2020 Q4 Renewable Energy, Sustainability and the Environment 2020 Q4 Energy Engineering and Power Technology 2020 Q4 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Llombart, A. (orcid)0000-0001-6350-4474 Melero, J.J. Universidad de Zaragoza (orcid)0000-0003-2360-0845 5009 535 Universidad de Zaragoza Dpto. Ingeniería Eléctrica Área Ingeniería Eléctrica 18 (2020), 128-133 Renewable energy power qual. j. Renewable Energy and Power Quality Journal 2172-038X 277665 http://zaguan.unizar.es/record/96193/files/texto_completo.pdf Versión publicada 560935 http://zaguan.unizar.es/record/96193/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:96193 articulos driver 2021-09-02-10:35:53 ARTICLE