dc.creatorCepeda, Cristian
dc.creatorOrozco-Henao, Cesar
dc.creatorPercybrooks, Winston
dc.creatorPulgarín-Rivera, Juan Diego
dc.creatorMontoya, Oscar Danilo
dc.creatorGil-González, Walter
dc.creatorVélez, Juan Carlos
dc.date.accessioned2020-09-10T21:20:58Z
dc.date.accessioned2022-09-28T20:09:53Z
dc.date.available2020-09-10T21:20:58Z
dc.date.available2022-09-28T20:09:53Z
dc.date.created2020-09-10T21:20:58Z
dc.date.issued2020-03-06
dc.identifierCepeda, C .; Orozco-Henao, C .; Percybrooks, W .; Pulgarín-Rivera, JD; Montoya, OD; Gil-González, W .; Vélez, JC Sistema inteligente de detección de fallas para microrredes. Energías 2020 , 13 , 1223.
dc.identifierhttps://hdl.handle.net/20.500.12585/9371
dc.identifierhttps://www.mdpi.com/1996-1073/13/5/1223
dc.identifier10.3390/en13051223
dc.identifier1996-1073
dc.identifierUniversidad Tecnológica de Bolívar
dc.identifierRepositorio Universidad Tecnológica de Bolívar
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3721379
dc.description.abstractThe dynamic features of microgrid operation, such as on-grid/off-grid operation mode, the intermittency of distributed generators, and its dynamic topology due to its ability to reconfigure itself, cause misfiring of conventional protection schemes. To solve this issue, adaptive protection schemes that use robust communication systems have been proposed for the protection of microgrids. However, the cost of this solution is significantly high. This paper presented an intelligent fault detection (FD) system for microgrids on the basis of local measurements and machine learning (ML) techniques. This proposed FD system provided a smart level to intelligent electronic devices (IED) installed on the microgrid through the integration of ML models. This allowed each IED to autonomously determine if a fault occurred on the microgrid, eliminating the requirement of robust communication infrastructure between IEDs for microgrid protection. Additionally, the proposed system presented a methodology composed of four stages, which allowed its implementation in any microgrid. In addition, each stage provided important recommendations for the proper use of ML techniques on the protection problem. The proposed FD system was validated on the modified IEEE 13-nodes test feeder. This took into consideration typical features of microgrids such as the load imbalance, reconfiguration, and off-grid/on-grid operation modes. The results demonstrated the flexibility and simplicity of the FD system in determining the best accuracy performance among several ML models. The ease of design’s implementation, formulation of parameters, and promising test results indicated the potential for real-life applications.
dc.languageeng
dc.publisherCartagena de Indias
dc.publisherIngeniería Electrónica
dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAtribución-NoComercial 4.0 Internacional
dc.titleIntelligent fault detection system for microgrids


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