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Optimal productivity in solar power plants based on machine learning and engineering management

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dc.contributor.author Huerta Herrainz, Álvaro
dc.contributor.author Pliego Marugán, Alberto
dc.contributor.author García Márquez, Fausto Pedro
dc.date.accessioned 2018-11-07T09:25:12Z
dc.date.available 2018-11-07T09:25:12Z
dc.date.issued 2018
dc.identifier.citation Lecture Notes on Multidisciplinary Industrial Engineering es_ES
dc.identifier.uri http://hdl.handle.net/10578/18943
dc.description.abstract The complexity of solar power plants is constantly increasing. This sophistication includes the increasing number of solar panels installed and the technologies that are employed in the energy conversion systems. The new solar plants require advanced methods to ensure the availability of all the panels. This paper proposes a recurrent convolutional neural network algorithm for detecting failures, reducing the costs and the time of the inspections. The method is aimed to analyze the data provided by an unmanned aerial vehicle fitted with a thermographic camera. This system provides thermographic data and telemetry. A region-based recurrent convolutional neural network is trained by a previously created dataset. Once the neural network is trained, it is used as a hot spot detector. This detector will have employed the telemetry in order to identify the real panel that can be affected. es_ES
dc.format text/plain es_ES
dc.language.iso en es_ES
dc.publisher Springer es_ES
dc.rights info:eu-repo/semantics/closedAccess es_ES
dc.subject Renewable energy es_ES
dc.subject Reliability es_ES
dc.subject Solar photovoltaic es_ES
dc.subject Unmanned aerial vehicles es_ES
dc.subject Thermography es_ES
dc.subject Recurrent convolutional neural network es_ES
dc.title Optimal productivity in solar power plants based on machine learning and engineering management es_ES
dc.type info:eu-repo/semantics/article es_ES
dc.relation.projectID RTC-2016-5694-3 es_ES
dc.identifier.DOI https://doi.org/10.1007/978-3-319-93351-1_77

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