Abstract. Renewable energy is a clean and inexhaustible source of energy, so every year interest in the study and the search for improvements in production increases. Wind energy is one of the most used sources of energy, and therefore the need for predictive maintenance management to guarantee the reliability and operability of each of the wind turbines has become a great study opportunity. In this work, a fault detection system is developed by applying an anomaly detector based on principal component analysis (PCA), in order to state early warnings of possible faults in the main bearing. For the development of the model, SCADA data from a wind park in operation are utilized. The results obtained allow detection of failures even months before the fatal breakdown occurs. This model requires (to be constructed) only the use of healthy SCADA data, without the need to obtain the fault history or install additional equipment or sensors that require greater investment. In conclusion, this proposed strategy provides a tool for the planning and execution of predictive maintenance within wind parks.
Abstract. Renewable energy is a clean and inexhaustible source of energy, so every year interest in the study and the search for improvements in production increases. Wind energy is one of the most used and therefore the need for predictive maintenance management to guarantee the reliableness and operability of each of the wind turbines has become a great study opportunity. In this work, a fault detection system is developed by applying an anomaly detector based on principal component analysis (PCA), in order to state early warnings of possible faults in the main bearing. For the development of the model, SCADA (supervisory control and data acquisition) data from a wind park in operation are utilized. The results obtained allow detection of failures even months before the fatal breakdown occurs. This model requires (to be constructed) only the use of healthy SCADA data, without the need to obtain the fault history or install additional equipment or sensors that require greater investment. In conclusion, this proposed strategy provides a tool for the planning and execution of predictive maintenance within wind parks.
Offshore wind energy is increasingly being realized at deeper ocean depths where jacket foundations are now the greatest choice for dealing with the hostile environment. The structural stability of these undersea constructions is critical. This paper states a methodology to detect and classify damage in a jacket-type support structure for offshore wind turbines. Because of the existence of unknown external disturbances (wind and waves), standard structural health monitoring technologies, such as guided waves, cannot be used directly in this application. Therefore, using vibration-response-only accelerometer measurements, a methodology based on two in-cascade Siamese convolutional neural networks is proposed. The first Siamese network detects the damage (discerns whether the structure is healthy or damaged). Then, in case damage is detected, a second Siamese network determines the damage diagnosis (classifies the type of damage). The main results and claims of the proposed methodology are the following ones: (i) It is solely dependent on accelerometer sensor output vibration data, (ii) it detects damage and classifies the type of damage, (iii) it operates in all wind turbine regions of operation, (iv) it requires less data to train since it is built on Siamese convolutional neural networks, which can learn from very little data compared to standard machine/deep learning algorithms, (v) it is validated in a scaled-down experimental laboratory setup, and (vi) its feasibility is demonstrated as all computed metrics (accuracy, precision, recall, and F1 score) for the obtained results remain above 96%.
El presente artículo presenta una revisión de la literatura la cual está enfocada en determinar el grado de importancia que tienen los sistemas de control para la gestión energética en micro-redes. Se describen las principales razones por las que se lleva a cabo el proceso de migración de plantas de uso de combustible fósil hacia plantas industriales de energía renovables, enfatizando en algunos tipos de energía renovable existentes. Adicionalmente, se resumen las técnicas de control existentes, entre las que figuran el control óptimo y jerárquico, para las micro-redes. Asimismo, se esbozan las principales tecnologías utilizadas en la actualidad para la implementación de sistemas de control predictivo basado en modelos (MPC, siglas en inglés) y el control económico predictivo basado en modelos (EMPC siglas en inglés). En este último, se realiza un análisis en términos económicos en función del coste.
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