2026-05-05T18:44:31Zhttps://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/630382025-02-21T12:54:13Zcom_10324_1169com_10324_931com_10324_894col_10324_1363
Elvira Ortiz, David Alejandro
Saucedo Dorantes, Juan José
Osornio Ríos, Roque Alfredo
Moríñigo Sotelo, Daniel
Antonino Daviu, Jose A.
2023-11-16T13:10:00Z
2023-11-16T13:10:00Z
2022
Electronics, 2022, Vol. 11, Nº. 2, 287
2079-9292
https://uvadoc.uva.es/handle/10324/63038
10.3390/electronics11020287
287
2
Electronics
11
2079-9292
Producción Científica
Wind generation has recently become an essential renewable power supply option. Wind generators are integrated with electrical machines that require correct functionality. However, the increasing use of non-linear loads introduces undesired disturbances that may compromise the integrity of the electrical machines inside the wind generator. Therefore, this work proposes a five-step methodology for power quality disturbance detection in grids with injection of wind farm energy. First, a database with synthetic signals is generated, to be used in the training process. Then, a multi-domain feature estimation is carried out. To reduce the problematic dimensionality, the features that provide redundant information are eliminated through an optimized feature selection performed by means of a genetic algorithm and the principal component analysis. Additionally, each one of the characteristic feature matrices of every considered condition are modeled through a specific self-organizing map neuron grid so they can be shown in a 2-D representation. Since the SOM model provides a pattern of the behavior of every disturbance, they are used as inputs of the classifier, based in a softmax layer neural network that performs the power quality disturbance detection of six different conditions: healthy or normal, sag or swell voltages, transients, voltage fluctuations and harmonic distortion. Thus, the proposed method is validated using a set of synthetic signals and is then tested using two different sets of real signals from an IEEE workgroup and from a wind park located in Spain.
Universidad Autónoma de Querétaro, Fondo Para El Desarrollo Del Conocimiento (FONDEC-UAQ 2020) - (project FIN202011)
Ministerio de Ciencia, Innovación y Universidades y Fondo Europeo de Desarrollo Regional (FEDER) - (project PGC2018-095747-B-I00)
application/pdf
eng
MDPI
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
© 2022 The Authors
Atribución 4.0 Internacional
Artificial intelligence
Electric machinery
Máquinas eléctricas
Electric generators
Generadores eléctricos
Renewable energy resources
Energías renovables
Wind power
Energía eólica
Optimization
Self-organizing maps
Electric power systems - Quality control
Energía eléctrica - Distribución - Calidad - Control
Electrical Engineering
Ingeniería eléctrica
1203.04 Inteligencia Artificial
2202.03 Electricidad
Power quality monitoring strategy based on an optimized multi-domain feature selection for the detection and classification of disturbances in wind generators
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
https://www.mdpi.com/2079-9292/11/2/287
SI