25th International Congress on Condition Monitoring and Diagnostics Engineering Management - COMADEM 2012, Huddersfield (United Kingdom). 18-20 June 2012
Summary:
Usually small and mid-sized photovoltaic solar power plants are located in rural areas and typically they operate unattended. Some technicians are in charge of the supervision of these plants and, if an alarm is automatically issued, they try to investigate the problem and correct it. Sometimes these anomalies are detected some hours or days after they begin. Also
the analysis of the causes once the anomaly is detected can take some additional time. All these factors motivated the development of a methodology able to perform continuous and automatic monitoring of the basic parameters of a photovoltaic solar power plant in order to detect anomalies as soon as possible, to diagnose their causes, and to immediately inform the
personnel in charge of the plant. The methodology proposed starts from the study of the most significant failure modes of a photovoltaic plant through a FMEA and using this information,
its typical performance is characterized by the creation of its normal behaviour models. They are used to detect the presence of a failure in an incipient or current form. Once an anomaly is
detected, an automatic and intelligent diagnosis process is started in order to investigate the possible causes. The paper will describe the main features of a software tool able to detect
anomalies and to diagnose them in a photovoltaic solar power plant.
DOI: 
https://doi.org/10.1088/1742-6596/364/1/012119
Published in Journal of Physics: Conference Series, vol: 364, pp: 012119-1/012119-12, ISSN: 1742-6588
Publication date: 2012-06-18.
Citation:
M.A. Sanz-Bobi, A. Muñoz, A. de Marcos, M. Bada, Intelligent system for a remote diagnosis of a photovoltaic solar power plant, 25th International Congress on Condition Monitoring and Diagnostics Engineering Management - COMADEM 2012, Huddersfield (United Kingdom). 18-20 June 2012. In: Journal of Physics: Conference Series, vol. 364, ISSN: 1742-6588
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