2026-05-05T22:01:42Zhttps://uvadoc.uva.es/oai/request

oai: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. 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 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. 2023-11-16 2023-11-16 2022 info:eu-repo/semantics/article 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 eng https://www.mdpi.com/2079-9292/11/2/287 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2022 The Authors Atribución 4.0 Internacional MDPI