Diagnóstico de fallos en generadores tipo jaula de ardilla de turbinas eólicas mediante la señal de corriente

In relation to maintenance, the main strategy of the wind industry is predictive maintenance based on the constant monitoring of various types of signals obtained from the components of the wind turbines (WTs) through sensors. Since all dynamic equipment produces acoustic or ultrasound vibration, this type of signal is generally used to monitor from the blades to the tower, and most of the existing references on fault detection and diagnosis use the vibration signal. However, there is a lack of publications on other types of signals, especially when it comes to field work. Therefore, this thesis is dedicated exclusively to the study of the current signal and its application to the maintenance of the squirrel-cage induction generator used in WTs. The research includes from the historical aspects of the use of the current signal, theoretical foundations on how the components associated with faults are manifested in the signal spectrum and the methodologies for detection and diagnosis, ranging from techniques for signal processing and traditional artificial intelligence (AI) models, to deep learning models, which represent the state of the art in AI modelsEn relación con el mantenimiento, la principal estrategia de la industria eólica es el mantenimiento predictivo basado en el monitoreo constante de varios tipos de señales obtenidas de los componentes de las turbinas eólicas (TEs) mediante sensores. Como todos los equipos dinámicos producen vibración acústica o ultrasonido, este tipo de señal es la que se utiliza generalmente para monitorear desde las palas hasta la torre, y la mayoría de las referencias existentes sobre detección y diagnóstico de fallos utilizan la señal de vibración. Sin embargo, existe una carencia de publicaciones sobre otro tipo de señales, especialmente cuando se trata de trabajos de campo. Por lo expuesto, esta tesis se dedica exclusivamente al estudio de la señal de corriente y su aplicación al mantenimiento del generador de inducción tipo jaula de ardilla utilizado en TEs. La investigación incluye desde los aspectos históricos del uso de la señal de corriente, fundamentos teóricos sobre cómo se manifiestan en el espectro de la señal las componentes asociadas a fallos y las metodologías para la detección y diagnóstico, abarcando desde las técnicas para procesamiento de señales y modelos de inteligencia artificial (IA) tradicionales, hasta los modelos de aprendizaje profundo, que representan el estado del arte en modelos de IAEscuela de DoctoradoDoctorado en Ingeniería Industria

Maintenance models applied to wind turbines. A comprehensive overview

Producción CientíficaWind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models

Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks

Producción CientíficaTo ensure the profitability of the wind industry, one of the most important objectives is to minimize maintenance costs. For this reason, the components of wind turbines are continuously monitored to detect any type of failure by analyzing the signals measured by the sensors included in the condition monitoring system. Most of the proposals for the detection and diagnosis of faults based on signal processing and artificial intelligence models use a fault-free signal and a signal acquired on a system in which a fault has been provoked; however, when the failures are incipient, the frequency components associated with the failures are very close to the fundamental component and there are incomplete data, the detection and diagnosis of failures is difficult. Therefore, the purpose of this research is to detect and diagnose failures of the electric generator of wind turbines in operation, using the current signal and applying generative adversarial networks to obtain synthetic data that allow for counteracting the problem of an unbalanced dataset. The proposal is useful for the detection of broken bars in squirrel cage induction generators, which, according to the control system, were in a healthy state

Wind resource assessment on Puná Island

Producción CientíficaPuná Island, located in the Pacific Ocean off the southern coast of Ecuador, has a population of approximately 3344 inhabitants. However, not all inhabitants have access to electricity, which is largely supplied by diesel generators. Therefore, to identify a renewable, sustainable, environmentally friendly and low-cost alternative, a 40-m-high anemometer tower was installed for wind resource assessment and to determine the possibility of generating electricity from wind energy. Based on mathematical models for electricity generation from wind energy, data were analyzed using the software Windographer and WAsP, to determine a long-term wind speed of 4.8 m/s and a mean wind power density of 272 W/m2. By simulating the use of a 3.3-MW wind turbine, we demonstrated that as much as 800 kWh could be generated during the hours when the wind reaches its highest speed. In addition to demonstrating the technical feasibility of meeting the electricity demands of Puná Island through wind power, this study exemplifies a method that can be used for wind resource assessment in any location