dc.creator | León de Mora, Carlos | es |
dc.creator | López Ojeda, Antonio | es |
dc.creator | Monedero Goicoechea, Iñigo Luis | es |
dc.creator | Montaño, Juan C. | es |
dc.date.accessioned | 2024-01-30T11:42:08Z | |
dc.date.available | 2024-01-30T11:42:08Z | |
dc.date.issued | 2001-06-12 | |
dc.identifier.citation | León de Mora, C., López Ojeda, A., Monedero Goicoechea, I.L. y Montaño, J.C. (2001). Classification of Disturbances in Electrical Signals Using Neural Networks. En Bio-Inspired Applications of Connectionism (IWANN 2001) (728-737), Granada (España): Springer. | |
dc.identifier.isbn | 978-3-540-42237-2 | es |
dc.identifier.isbn | 978-3-540-45723-7 (online) | es |
dc.identifier.uri | https://hdl.handle.net/11441/154228 | |
dc.description.abstract | This paper describes a currently project accomplished by the authors in the area of Power Quality (PQ) using artificial neural networks (ANN). The efforts are oriented to obtain a product (Power disturbances monitor for threephase systems) that permits a real time detection, automatic classification, and record process of impulsive or oscillatory voltage transients, long term disturbances, and waveform distortions in electrical three-phase AC signals. To classify the electrical disturbances, we consider using a fully connected feedforward ANN with a backpropagation learning method based on Generalized Delta Rule. In order to select the best alternative more than 200 network architectures were tested. Long-term disturbances, like swells or longduration interruptions, have been detected using a method based on the test of the RMS value of the signal. Short-term disturbances, like sags, are detected by sampling a cycle of the electrical signal, and waveform distortions are detected using the main harmonics of the signal. To train the ANN we have developed a three-phase virtual generator of electrical disturbances. In order to compress the ANN input data we use the Wavelet Transform. | es |
dc.format | application/pdf | es |
dc.format.extent | 10 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | Bio-Inspired Applications of Connectionism (IWANN 2001) (2001), pp. 728-737. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Artificial Neural Network | es |
dc.subject | Mean Square Error | es |
dc.subject | Discrete Wavelet Transform | es |
dc.subject | Training Pattern | es |
dc.subject | Power Quality | es |
dc.title | Classification of Disturbances in Electrical Signals Using Neural Networks | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Tecnología Electrónica | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/3-540-45723-2_88 | es |
dc.identifier.doi | 10.1007/3-540-45723-2_88 | es |
dc.publication.initialPage | 728 | es |
dc.publication.endPage | 737 | es |
dc.eventtitle | Bio-Inspired Applications of Connectionism (IWANN 2001) | es |
dc.eventinstitution | Granada (España) | es |