Detection of jacket offshore wind turbine structural damage using an 1D-convolutional neural network with a support vector machine layer

Because offshore wind turbines, particularly their foundations, operate in hostile environments, implementing a structural health monitoring system is one of the best ways to monitor their condition, schedule maintenance, and predict possible fatal failures at lower costs. A novel strategy for detecting damage in offshore wind turbine jacket foundations is developed in this work, based on a vibration monitoring methodology that reshapes the data into a multichannel array, with as many channels as correlated sensors with the predicted variable, a 1-D deep convolutional neural network to extract temporal features from the monitored data, and a support vector machine as a final classification layer. The obtained model allows the detection of three types of bar states: healthy bar, cracked bar, and bar with an unlocked bolt.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Fault tolerant control design of floating offshore wind turbines

This work is concerned with active vibration mitigation in wind turbines (WT) but not through the use of specifically tailored devices. Instead, a general control scheme is designed for torque and pitch controllers based on a super-twisting algorithm, which uses additional feedback of the fore-aft and side-to-side acceleration signals at the top of the WT tower to mitigate the vibrational behavior. In general, proposed methods to improve damping through pitch and torque control suffer from increased blade pitch actuator usage. However, in this work the blade pitch angle is smoothed leading to a decrease of the pitch actuator effort, among other benefits evidenced through numerical experiments. The most frequent faults induce vibrations in the corresponding WT subsystems. In fact, vibration monitoring has been recently used for fault diagnosis Thus, by means of vibration mitigation, different faulty conditions can be alleviated leading to a passive fault tolerant control. In this work, coupled non-linear aero-hydro- servo-elastic simulations of a floating offshore wind turbine are carried out for one of the most common pitch actuator faults.Postprint (published version

Wind turbine main bearing failure prediction using a hybrid neural network

Energy is necessary for economic growth and improved well-being, but it poses a great challenge to be generated without increasing costs and avoiding pollution. A viable option is wind energy because it is a clean and renewable. However, continuous monitoring and maintenance of wind turbines is required for the further development of wind farms. Main bearing failures were identified by the European Academy of Wind Energy as a critical issue in terms of increasing the availability and reliability of wind turbines. In this work, it is proposed a hybrid neural network for main bearing failure prognosis. This network consists of a two-dimensional convolutional neural network (to extract spatial-temporal characteristics from the data) sequentially connected with a long short-term memory network (to learn sequence patterns) to predict the slow-speed shaft temperature (the closest temperature to the main bearing). The mean square error between its real measurement and its prediction gives a failure indicator. When it is greater than a defined threshold, then an alarm is triggered and gives the maintenance staff time to check the component. The advantage of this strategy is that it does not need faulty data to be trained, since it is based on a normality model, that is, it is trained with a single class of data (healthy) and does not require incurring high costs per acquisition of new sensors since SCADA data is used (which comes in all industrial size turbine). The results show that the use of a hybrid network can identify failures around four months before a fatal failure occurs.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Hysteresis-based design of dynamic reference trajectories to avoid saturation in controlled wind turbines

The main objective of this paper is to design a dynamic reference trajectory based on hysteresis to avoid saturation in controlled wind turbines. Basically, the torque controller and pitch controller set-points are hysteretically manipulated to avoid saturation and drive the system with smooth dynamic changes. Simulation results obtained from a 5MW wind turbine benchmark model show that our proposed strategy has a clear added value with respect to the baseline controller (a well-known and accepted industrial wind turbine controller). Moreover, the proposed strategy has been tested in healthy conditions but also in the presence of a realistic fault where the baseline controller caused saturation to nally conduct to instability.Peer ReviewedPostprint (author's final draft

Wind turbines controllers design based on the super-twisting algorithm

The continuous increase in the size of wind turbines (WTs) has led to new challenges in the design of novel torque and pitch controllers. Today’s WT control design must fulfill numerous specifications to assure effective electrical energy production and to hold the tower vibrations inside acceptable levels of operation. Hence, this paper presents modern torque and pitch control developments based on the super-twisting algorithm (STA) by using feedback of the fore- aft and side-to-side acceleration signals of the WT tower. According to numerical experiments realized using FAST, these controllers mitigate vibrations in the tower without affecting the quality of electrical power production. Moreover, the proposed controllers’ performance is better than the baseline controllers used for comparison.Postprint (author's final draft

Acceleration-based fault-tolerant control design of offshore fixed wind turbines

Wind turbines (WTs) are basically controlled by varying the generator load torque (with the so-called torque control) and the blade pitch angles (with the so-called pitch control) based on measurement of the generator shaft speed. These two controllers unitedly work to satisfy the control objectives, and it is crucial that they are tolerant to possible faults in the WT system. Passive fault-tolerant control comprises the design of robust controllers against disturbances and uncertainties. This enables the controller to counteract the effect of a fault without requiring reconfiguration or fault detection. In this regard, the main contribution of this paper is to propose new control techniques that not only provide fault tolerance capabilities to the WT system but also improve the overall performance of the system in both fault-free and faulty conditions. Coupling nonlinear aero-hydro-servo-elastic simulations of an offshore WT with jacket platform is carried out for several pitch actuator faults. The jacket platform motions and structural loads caused by fault events with the proposed controllers are compared with loads encountered during normal operation and with respect to a well-known baseline controller in the literature. The proposed controllers are based in the super-twisting algorithm by using feedback of the generator shaft speed as well as the fore-aft and side-to-side acceleration signals of the WT tower.Preprin

Fault detection and fault tolerant control in wind turbines

Renewable energy is an important sustainable energy in the world. Up to now, as an essential part of low emissions energy in a lot of countries, renewable energy has been important to the national energy security, and played a significant role in reducing carbon emissions. It comes from natural resources, such as wind, solar, rain, tides, biomass, and geothermal heat. Among them, wind energy is rapidly emerging as a low carbon, resource efficient, cost effective sustainable technology in the world. Due to the demand of higher power production installations with less environmental impacts, the continuous increase in size of wind turbines and the recently developed offshore (floating) technologies have led to new challenges in the wind turbine systems.Wind turbines (WTs) are complex systems with large flexible structures that work under very turbulent and unpredictable environmental conditions for a variable electrical grid. The maximization of wind energy conversion systems, load reduction strategies, mechanical fatigue minimization problems, costs per kilowatt hour reduction strategies, reliability matters, stability problems, and availability (sustainability) aspects demand the use of advanced (multivariable and multiobjective) cooperative control systems to regulate variables such as pitch, torque, power, rotor speed, power factors of every wind turbine, etc. Meanwhile, with increasing demands for efficiency and product quality and progressing integration of automatic control systems in high-cost and safety-critical processes, the fields of fault detection and isolation (FDI) and fault tolerant control (FTC) play an important role. This thesis covers the theoretical development and also the implementation of different FDI and FTC techniques in WTs. The purpose of wind turbine FDI systems is to detect and locate degradations and failures in the operation of WT components as early as possible, so that maintenance operations can be performed in due time (e.g., during time periods with low wind speed). Therefore, the number of costly corrective maintenance actions can be reduced and consequently the loss of wind power production due to maintenance operations is minimized. The objective of FTC is to design appropriate controllers such that the resulting closed-loop system can tolerate abnormal operations of specific control components and retain overall system stability with acceptable system performance. Different FDI and FTC contributions are presented in this thesis and published in different JCR-indexed journals and international conference proceedings. These contributions embrace a wide range of realistic WTs faults as well as different WTs types (onshore, fixed offshore, and floating). In the first main contribution, the normalized gradient method is used to estimate the pitch actuator parameters to be able to detect faults in it. In this case, an onshore WT is used for the simulations. Second contribution involves not only to detect faults but also to isolate them in the pitch actuator system. To achieve this, a discrete-time domain disturbance compensator with a controller to detect and isolate pitch actuator faults is designed. Third main contribution designs a super-twisting controller by using feedback of the fore-aft and side-to-side acceleration signals of the WT tower to provide fault tolerance capabilities to the WT and improve the overall performance of the system. In this instance, a fixed-jacket offshore WT is used. Throughout the aforementioned research, it was observed that some faults induce to saturation of the control signal leading to system instability. To preclude that problem, the fourth contribution of this thesis designs a dynamic reference trajectory based on hysteresis. Finally, the fifth and last contribution is related to floating-barge WTs and the challenges that this WTs face. The performance of the proposed contributions are tested in simulations with the aero-elastic code FAST.La energía renovable es una energía sustentable importante en el mundo. Hasta ahora, como parte esencial de la energía de bajas emisiones en muchos países, la energía renovable ha sido importante para la seguridad energética nacional, y jugó un papel importante en la reducción de las emisiones de carbono. Proviene de recursos naturales, como el viento, la energía solar, la lluvia, las mareas, la biomasa y el calor geotérmico. Entre ellos, la energía eólica está emergiendo rápidamente como una tecnología sostenible de bajo carbono, eficiente en el uso de los recursos y rentable en el mundo. Debido a la demanda de instalaciones de producción de mayor potencia con menos impactos ambientales, el aumento continuo en el tamaño de las turbinas eólicas y las tecnologías offshore (flotantes) recientemente desarrolladas han llevado a nuevos desafíos en los sistemas de turbinas eólicas. Las turbinas eólicas son sistemas complejos con grandes estructuras flexibles que funcionan en condiciones ambientales muy turbulentas e impredecibles para una red eléctrica variable. La maximización de los sistemas de conversión de energía eólica, los problemas de minimización de la fatiga mecánica, los costos por kilovatios-hora de estrategias de reducción, cuestiones de confiabilidad, problemas de estabilidad y disponibilidad (sostenibilidad) exigen el uso de sistemas avanzados de control cooperativo (multivariable y multiobjetivo) para regular variables tales como paso, par, potencia, velocidad del rotor, factores de potencia de cada aerogenerador, etc. Mientras tanto, con las crecientes demandas de eficiencia y calidad del producto y la progresiva integración de los sistemas de control automático en los procesos de alto costo y de seguridad crítica, los campos de detección y aislamiento de fallos (FDI) y control tolerante a fallos (FTC) juegan un papel importante. Esta tesis cubre el desarrollo teórico y también la implementación de diferentes técnicas de FDI y FTC en turbinas eólicas. El propósito de los sistemas FDI es detectar y ubicar las degradaciones y fallos en la operación de los componentes tan pronto como sea posible, de modo que las operaciones de mantenimiento puedan realizarse a su debido tiempo (por ejemplo, durante periodos con baja velocidad del viento). Por lo tanto, se puede reducir el número de costosas acciones de mantenimiento correctivo y, en consecuencia, se reduce al mínimo la pérdida de producción de energía eólica debido a las operaciones de mantenimiento. El objetivo de la FTC es diseñar controladores apropiados de modo que el sistema de bucle cerrado resultante pueda tolerar operaciones anormales de componentes de control específicos y retener la estabilidad general del sistema con un rendimiento aceptable del sistema. Diferentes contribuciones de FDI y FTC se presentan en esta tesis y se publican en diferentes revistas indexadas a JCR y en congresos internacionales. Estas contribuciones abarcan una amplia gama de fallos WTs realistas, así como diferentes tipos de turbinas (en tierra, en alta mar ancladas al fondo del mar y flotantes). El rendimiento de las contribuciones propuestas se prueba en simulaciones con el código aeroelástico FAST.Postprint (published version

PCA-based accelerometer data transformation in offshore jacket-type wind turbine support structures for incipient damage detection

Global power capacity is increasingly being comprised by renewable energy sources, where wind farms stand out as paramount power stations. Therefore, the structural health of wind turbines (WTs) represents an essential factor in the energy industry. Specifically, offshore jacket-type WT supports are under critical operational and environmental conditions. Hence, a damage detection strategy is stated, considering several types of structural states and limitations in the quantity of acquired data. The proposed methodology consists of a PCA-based data transformation, in which initially known healthy data are used to be compared with a set of data to be diagnosed; then the damage or healthy states are predicted based on the Mahalanobis distance and threshold value. Because it is a semi-supervised technique, there is no requirement to have damage data on hand to construct the model. The strategy is tested in a scaled-down WT experimental tower.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Damage detection and identification in jacket-type support structures via deep learning and accelerometer sensors data

Postprint (published version

Wind turbine multi-fault detection and classification based on SCADA data

Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a data-driven multi-fault detection and classification strategy is developed. An advanced wind turbine benchmark is used. The wind turbine we consider is subject to different types of faults on actuators and sensors. The main challenges of the wind turbine fault detection lie in their non-linearity, unknown disturbances, and significant measurement noise at each sensor. First, the SCADA measurements are pre-processed by group scaling and feature transformation (from the original high-dimensional feature space to a new space with reduced dimensionality) based on multiway principal component analysis through sample-wise unfolding. Then, 10-fold cross-validation support vector machines-based classification is applied. In this work, support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results showed that all studied faults were detected and classified with an overall accuracy of 98.2%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for online (real-time) condition monitoring in wind turbines.Postprint (published version