Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)

Wind turbine condition monitoring : technical and commercial challenges.

Deployment of larger scale wind turbine systems, particularly offshore, requires more organized operation and maintenance strategies to ensure systems are safe, profitable and cost-effective. Among existing maintenance strategies, reliability centred maintenance is regarded as best for offshore wind turbines, delivering corrective and proactive (i.e. preventive and predictive) maintenance techniques enabling wind turbines to achieve high availability and low cost of energy. Reliability centred maintenance analysis may demonstrate that an accurate and reliable condition monitoring system is one method to increase availability and decrease the cost of energy from wind. In recent years, efforts have been made to develop efficient and cost-effective condition monitoring techniques for wind turbines. A number of commercial wind turbine monitoring systems are available in the market, most based on existing techniques from other rotating machine industries. Other wind turbine condition monitoring reviews have been published but have not addressed the technical and commercial challenges, in particular, reliability and value for money. The purpose of this paper is to fill this gap and present the wind industry with a detailed analysis of the current practical challenges with existing wind turbine condition monitoring technology

Aeronautical engineering: A continuing bibliography, supplement 122

This bibliography lists 303 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1980

mechanical behaviour of wind turbines operating above design conditions

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Prognostics and health aware model predictive control of wind turbines

Wind turbines components are subject to considerable stresses and fatigue due to extreme environmental conditions to which they are exposed, especially those located offshore. Also, the most common faults present in wind turbine components have been investigated for years by the research community and that has led to propose a fault diagnosis and fault tolerant control wind turbine benchmark which include a set of faults that affect the sensors and actuators of several wind turbine components. This thesis presents some contributions to the fields of fault diagnosis, fault-tolerant control, prognostics and its integration with wind turbine control which leads to proposing a control approach called health-aware model predictive control (HAMPC). The contributions are summarized below: - Model-based fault diagnosis: to perform fault detection and isolation interval-based observers together with a set of analytical redundant relations (ARRs) are obtained based on a structural analysis and the fault signature matrix that relates the ARRs with the faults. - Fault tolerant control: it is proposed a fault tolerant control scheme that integrates fault detection and an algorithm for fault accommodation. The scheme has the objective to avoid the increment of blades and tower loads when a fault in the rotor azimuth angle sensor occurs using the individual pitch control technique (IPC). - Wind turbine blades fatigue prognostics and degradation: fatigue is assessed using the rainflow counting algorithm which is used to estimate the accumulated damage and for degradation, it is used a stiffness degradation model of blades material which is used to make predictions of remaining useful life (RUL). - Wind turbines health control: the module for the health of the system based on fatigue damage estimation and RUL predictions is integrated with model predictive control (MPC) leading to the proposed control approach (HAMPC). The contributions presented in this thesis have been validated on a wind turbine study case that uses a 5MW wind turbine reference model implemented in a high fidelity wind turbine simulator (FAST).Els components dels aerogeneradors estan sotmesos a considerable estrès i fatiga, degut a les condicions ambientals extremes a les quals estan exposats, especialment els localitzats en alta mar. Per aquest motiu, al comunitat científica durant els últims anys ha investigat les averies més comunes presents en els aerogeneradors, fet que ha portat a proposar un cas d'estudi de diagnosi i control tolerant de fallades que inclou un conjunt de fallades que afecten a diversos components dels aerogeneradors. Aquesta tesi presenta algunes contribucions en els camps de la diagnosi de fallades, el control tolerant de fallades i la prognosi, així com la seva integració amb el control d'aerogeneradors, fet que ha portat a proposar una tècnica de control anomenada control predictiu basada en models conscients de la salut del sistema (HAMPC). Concretament les aportacions es poden resumir en: - Diagnosi de fallades basada en models: per a la detecció s'utilitzen observadors intervalars i l'aïllament de la fallada es fa en base el conjunt d'ARRs obtinguts de l'anàlisi estructural i de la matriu de signatures de fallades que relaciona les ARRs amb les fallades. - Control tolerant de fallades: es proposa un esquema de control tolerant a fallades que integra la detecció de fallades i algoritme d'acomodació de fallades, i té per objectiu evitar l'augment de càrregues en la pala i la torre quan es produeix una fallada en el sensor azimuth quan es fa un control individual de la inclinació de les pales (IPC). - Prognosi de la fatiga i la degradació de les pales: la fatiga s'avalua amb un algorisme denominat "rainflow counting" amb el qual es fa estimació del dany acumulat i per a la degradació es fa servir un model de degradació de la rigidesa del material amb el qual es fan prediccions de la vida útil restant (RUL). - Control de la salut d'aerogeneradors: s'ha integrat la gestió de la salut del sistema basat en danys per fatiga o prediccions de RUL amb control predictiu basat en models (MPC) donant lloc al control que anomenem HAMPC. Les contribucions presentades en aquesta tesi han sigut validades en un cas d'estudi d'aerogeneradors basat en un aerogenerador de referència de 5MW de potència implementat en el simulador d'aerogeneradors d'alta fidelitat conegut amb el nom de FAST.Postprint (published version

Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years

A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000–2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well

Integrating Systems Health Management with Adaptive Controls for a Utility-Scale Wind Turbine

Increasing turbine up-time and reducing maintenance costs are key technology drivers for wind turbine operators. Components within wind turbines are subject to considerable stresses due to unpredictable environmental conditions resulting from rapidly changing local dynamics. Systems health management has the aim to assess the state-of-health of components within a wind turbine, to estimate remaining life, and to aid in autonomous decision-making to minimize damage. Advanced adaptive controls can provide the mechanism to enable optimized operations that also provide the enabling technology for Systems Health Management goals. The work reported herein explores the integration of condition monitoring of wind turbine blades with contingency management and adaptive controls. Results are demonstrated using a high fidelity simulator of a utility-scale wind turbine