RT info:eu-repo/semantics/article
T1 Power quality monitoring strategy based on an optimized multi-domain feature selection for the detection and classification of disturbances in wind generators
A1 Elvira Ortiz, David Alejandro
A1 Saucedo Dorantes, Juan José
A1 Osornio Ríos, Roque A.
A1 Moríñigo Sotelo, Daniel
A1 Antonino Daviu, Jose A.
K1 Artificial intelligence
K1 Electric machinery
K1 Máquinas eléctricas
K1 Electric generators
K1 Generadores eléctricos
K1 Renewable energy resources
K1 Energías renovables
K1 Wind power
K1 Energía eólica
K1 Optimization
K1 Self-organizing maps
K1 Electric power systems - Quality control
K1 Energía eléctrica - Distribución - Calidad - Control
K1 Electrical Engineering
K1 Ingeniería eléctrica
K1 1203.04 Inteligencia Artificial
K1 2202.03 Electricidad
AB 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.
PB MDPI
SN 2079-9292
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/63038
UL https://uvadoc.uva.es/handle/10324/63038
LA eng
NO Electronics, 2022, Vol. 11, Nº. 2, 287
NO Producción Científica
DS UVaDOC
RD 23-dic-2024