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dc.contributor.author | Elvira Ortiz, David Alejandro | |
dc.contributor.author | Moríñigo Sotelo, Daniel | |
dc.contributor.author | Duque Pérez, Óscar | |
dc.contributor.author | Jaen Cuellar, Arturo Yosimar | |
dc.contributor.author | Osornio Ríos, Roque A. | |
dc.contributor.author | Romero-Troncoso, René de Jesús | |
dc.date.accessioned | 2024-01-20T16:26:51Z | |
dc.date.available | 2024-01-20T16:26:51Z | |
dc.date.issued | 2018-04-23 | |
dc.identifier.citation | IEEE Access, vol. 6, pp. 24035-24047, 2018, doi: 10.1109/ACCESS.2018.2829148. | es |
dc.identifier.issn | 2169-3536 | es |
dc.identifier.uri | https://uvadoc.uva.es/handle/10324/64785 | |
dc.description.abstract | Flicker is a very common power quality disturbance due to the inclusion of photovoltaic (PV) generation on the electric grid. This paper presents a methodology for flicker estimation in a PV generation that fuses multiple signal classification and discrete wavelet transform to provide high-resolution frequency estimation with an accurate amplitude measurement. This tool considers that flicker is not stationary over time and that more than one frequency component can exist on a voltage signal. In Addition, this paper finds that sun irradiance, temperature, and the action of the solar inverter are the sources of flicker in PV generation. The methodology is applied to real signals from three days with different weather conditions. In Addition, two different solar inverters are evaluated to see their influence on the parameters of flicker. Results show that flicker can contain more than one frequency component that can change over time. Finally, this paper shows that around 70% to 80% of flicker is linked to irradiance and cell temperature whereas the 20% to 30% can be attributed to the operation of solar inverters. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | The IEEE | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.subject.classification | Discrete wavelet transforms | es |
dc.subject.classification | flickr | es |
dc.subject.classification | multiple signal classification | es |
dc.subject.classification | photovoltaic systems | es |
dc.subject.classification | power quality | es |
dc.title | Methodology for Flicker Estimation and Its Correlation to Environmental Factors in Photovoltaic Generation | es |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.holder | Los autores | es |
dc.identifier.doi | https://doi.org/10.1109/ACCESS.2018.2829148 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8344755 | es |
dc.identifier.publicationfirstpage | 24035 | es |
dc.identifier.publicationlastpage | 24047 | es |
dc.identifier.publicationtitle | IEEE Access | es |
dc.identifier.publicationvolume | 6 | es |
dc.peerreviewed | SI | es |
dc.description.project | 10.13039/501100003141-CONACYT Scholarship (Grant Number: 415315) | es |
dc.description.project | FOMIX (Grant Number: QUERETARO-2014-C03-250269) | es |
dc.description.project | CEI-Triangular, Universidades de Burgos, León y Valladolid | es |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es |