Machine Learning for Wind Turbine Blades Maintenance Management

Delamination in Wind Turbine Blades (WTB) is a common structural problem that can generate large costs. Delamination is the separation of layers of a composite material, which produces points of stress concentration. These points suffer greater traction and compression forces in working conditions, and they can trigger cracks, and partial or total breakage of the blade. Early detection of delamination is crucial for the prevention of breakages and downtime. The main novelty presented in this paper has been to apply an approach for detecting and diagnosing the delamination WTB. The approach is based on signal processing of guided waves, and multiclass pattern recognition using machine learning. Delamination was induced in the WTB to check the accuracy of the approach. The signal is denoised by wavelet transform. The autoregressive Yule–Walker model is employed for feature extraction, and Akaike’s information criterion method for feature selection. The classifiers are quadratic discriminant analysis, k-nearest neighbors, decision trees, and neural network multilayer perceptron. The confusion matrix is employed to evaluate the classification, especially the receiver operating characteristic analysis by: recall, specificity, precision, and F-score

Ingenium Research Group, Universidad de Castilla-La Mancha Ciudad Real, Edificio Politécnica 13071, Spain [email protected]

Renewable energy is being one of the options to cover the demand due to the environmental restrictions. One of the most relevant renewable energy sources is the solar energy, where the concentrated solar power is nowadays the source that is getting more importance. The correct performance of solar receiver is crucial because its failure can result in significant costs and availability of the energy service. Non-destructive testing is broadly used in structural health monitoring systems in order to detect and diagnose faults/failures. The aim of this paper is to present a fault detection and diagnosis approach based on long range ultrasonic technology, together with novel analytical procedures on signal processing of high frequency waves (Lamb waves). These waves flow through the material via the piezoelectric transducers, where these transducers are also employed as sensors. The fault can be detected and diagnosed by changes in the signal when it is modified by the fault. The experimental platform consists of: i) a data logger able to generate and read voltage signals at high frequency; ii) a sensing system based on piezoelectric transducers placed at the solar collector. A novel method of analyzing the data generated in the platform by means of time series is employed

Acoustic emission and signal processing for fault detection and location in composite materials

The renewable energy industry is in a constant improvement in order to compete and cover any evolving opportunity presented. Nowadays one of those remarkable competitive advantages is focused on maintenance management and terms as operating and maintenance costs, availability, reliability, safety, lifetime, etc. The objectives of this paper are focused on the blades of a wind turbine. A structural health monitoring study is presented, that starts with the collection and analysis of data coming from different non-destructive tests. Signals from acoustic emissions are studied by a novel signal processing approach to detect cracks on the surface of the blades. The case study proposes a new localization method using macro-fibre composite sensors and actuators. The monitoring system uses three sensors strategically located on the blade section. Among the main difficulties involved in this first approach, the modal separation of the wave is taken into account for its importance when drawing conclusions concerning the crack. This effect is the result of the blade breakdown, producing different signals at multiple frequencies. Another drawback is associated to the direction of the fibres in the composite material. This is known as slowness profile, a function depending on the propagation speed. On the other hand, the main novelty of the approach presented is that it is able to predict the failure. In addition, it can be considered an accurate analysis as the solution will be always a single point obtained from a graphical method, i.e. the location of the crack can be detected with precision. The results are also checked quantitatively using nonlinear equations

Optimization of the solar energy storage capacity for a monitoring UAV

Unmanned aerial vehicles integrate propulsion systems, communication modules, and sensors, allowing an operator to perform autonomous or remote-controlled flight actions. UAVs provide important advantages for exploring remote locations due to their cost-effectiveness and versatility compared to manned aircraft. However, addressing safety and flight autonomy challenges remains necessary. This paper analyzes and proposes the integration of a photovoltaic solar system to power UAV devices. Through a brief analysis of the aerodynamic model and the wing profile, a consolidation of the solar cells has been achieved without compromising efficiency in-flight maneuvers. Furthermore, an analysis is conducted on the potential of using photovoltaic solar resources in fixed-wing aircraft. The research aims to determine the optimal wing surface area required for video surveillance applications. The current model under discussion is a glider-type system that incorporates two distinct systems, one for video transmission and the other for telemetry data acquisition. An analysis of the battery charge and discharge pattern was carried out using computer simulation tools. This analysis aimed to optimize the battery charging process by integrating photovoltaic cells. The results and conclusions of the tests are described in the final section of the pape

OptiWindSeaPower: Gestión Integral Óptima de Parques Eólicos Offshore Mediante Nuevos Modelos Matemáticos (1º parte)

Las políticas de Unión Europea en energía y medio ambiente están dirigidas a impulsar y desarrollar plataformas eólicas offshore. Ello hace que el sistema eléctrico español vaya a depender, cada vez más, de este tipo de sistemas de generación eléctrica. Los aerogeneradores para este caso son de mayor tamaño, más complejos y, requieren de unas altas exigencias de seguridad, fiabilidad, disponibilidad y mantenibilidad. En este proyecto se aborda dicha problemática planteando como fin último la gestión ntegral y óptima de este tipo de parques de aerogeneradores. El proyecto que da origen a este artículo parte con la intención de continuar y completar el proyecto Nacional WindSeaEnergy (DPI2012-31579), donde se analizaron las referencias científicas más importantes en revistas de alto impacto, observándose que existen grandes carencias de modelos matemáticos que permitan analizar las señales que se están monitorizando mediante Ensayos No Destructivos (END) para determinar el estado de las estructuras, así como en la gestión óptima de los aerogeneradores y los parques eólicos. OptiWindSeaPower ha continuado y completado este estudio iniciado en el ámbito de los elementos rotativos del aerogenerador y de la gestión del mantenimiento de los mismos, haciendo un estudio más exhaustivo en los sistemas de monitorización y métodos de procesamientos de señales para los elementos estructurales de dichos equipos. Se han tomado como referencia datos de los proyectos nacionales IcingBlades y WindSeaEnergy, además de los proyectos Europeos OPTIMUS y NIMO. Se ha diseñado y desarrollado un banco de ensayos de un sistema de monitorización basado en END, en ultrasonidos, para determinar el estado estructural de los aerogeneradores para completar dicho conjunto de señales. Se ha elaborado un modelo del coste de ciclo de vida para el sistema predictivo de mantenimiento. Así como se propone emplear modelos matemáticos basados en el análisis en el tiempo, la frecuencia y tiempo/frecuencia, así como Transformadas Wavellets, Redes Neuronales/Inteligencia Artificial, métodos basados en la extracción de características de la señal y derivados de la función de transferencia del sistema. El análisis multivariable se ha realizado mediante Árboles de Decisión Lógicos, que se analizarán empleando Diagramas de Decisión Binarios, y las medidas de importancia creadas mediante métodos heurísticos. Esto permite controlar y optimizar el estado de un aerogenerador de forma íntegra. Finalmente se han creado nuevos índices de significancia basados en costes y se formula el problema de optimización y su resolución mediante métodos metaheurísticos para determinar la política óptima de inversiones en la gestión de este tipo de parques

Inspection and structural health monitoring techniques for concentrated solar power plants

Parabolic trough concentrators are the most widely deployed type of solar thermal power plant. The majority of parabolic trough plants operate up to 400 °C. However, recent technological advances involving molten salts instead of oil as working fluid the maximum operating temperature can exceed 550 °C. CSP plants face several technical problems related to the structural integrity and inspection of critical components such as the solar receivers and insulated piping of the coolant system. The inspection of the absorber tube is very difficult as it is covered by a cermet coating and placed inside a glass envelope under vacuum. Volumetric solar receivers are used in solar tower designs enabling increased operational temperature and plant efficiency. However, volumetric solar receiver designs inherently pose a challenging inspection problem for maintenance engineers due to their very complex geometry and characteristics of the materials employed in their manufacturing. In addition, the rest of the coolant system is insulated to minimise heat losses and therefore it cannot be inspected unless the insulation has been removed beforehand. This paper discusses the non-destructive evaluation techniques that can be employed to inspect solar receivers and insulated pipes as well as relevant research and development work in this field

New approaches on fault detection and diagnosis for structures manintenance management.

The renewable energy industry is in a constant improvement in order to cover the current demands. Within the renewables energies, wind energy and concentrated solar power (CSP) are two of the fastest growing sources of renewable energy production. Wind farms, in contrast to the conventional power plants, are exposed to the inclement and variability of weather. As a result of this variations, wind turbines are subjected to high mechanical loads, which require a high degree of maintenance to provide accost-effective power output and care the life cycle of the equipment [1-3]. Nowadays, the demand for wind energy continues raising at an exponential rate, due to the reduction in operating and maintenance costs and increasing reliability of wind turbines [4,5]. CSP is an alternative to onshore wind farms and photovoltaic which has received significant attention in recent years. Particularly, in southern countries, where the sunlight is and abundant resource. It is crucial to ensure that the solar receivers work properly to avoid failures, and to increase the reliability, availability, safety and maintainability. Both renewable energies have some monitoring systems that allow to know the status of critical components, and to determine anomalous operating situations. The power generation plants have incorporated a basic online monitoring control system. This system generally includes sensors for monitoring the machine parameters, such as temperature, speed, fluid levels, unbalance in the rotor, etc. [6] The Condition Based Maintenance (CBM) is and advanced maintenance strategy based on monitoring data on the machine status. It can obtain measurements of condition monitoring of wind turbine or CSP components [7-10]. The main objective of CBM is to optimize maintenance activities and to reduce costs. Non-Destructive Testing (NDT) is used in Structural Health Monitoring (SHM) systems for Fault Detection and Diagnosis (FDD). Within the CBM, some NDT techniques are used to prevent serious failures in critical components such as blades, gearbox, tower or receiver tubes. [11,12]. The techniques for condition monitoring employed in this work are based on the infrared radiometry and the ultrasonic guided waves. Infrared remote sensing techniques are based on the reception and analysis of the electromagnetic energy reflected or emitted by a surface. The Guided Waves are non-destructive techniques widely used for structural evaluation of plates or pipes. The aim of this work is to develop new approaches for condition monitoring of wind turbines and concentrated solar plants based on infrared technology and guided waves

A New Approach for Fault Detection, Location and Diagnosis by Ultrasonic Testing

Wind turbine blades are constantly submitted to different types of particles such as dirt, ice, etc., as well as all the different environmental parameters that affect the behaviour and efficiency of the energy generation system. These parameters can cause faults to the wind turbine blades, modifying their behaviour due, for example, to the turbulence. A new method is presented in this paper based on cross-correlations to determine the presence of delamination in the blades. The experiments were conducted in two real wind turbine blades to analyse the fault and non-fault blades using ultrasonic guided waves. Finally, the energy analysis of the signal based on wavelet transforms allowed to determine energies abrupt changes in the correlation of the signals and to locate the faults.Dirección General de Universidades, Investigación e Innovación of Castilla-La Mancha, under Research Grant ProSeaWind project (Ref.: SBPLY/19/180501/000102).3.004 JCR (2020) Q3, 70/114 Energy & Fuels0.598 SJR (2020) Q2, 39/886 Control and OptimizationNo IDR 2019UE

Energy Environment Maintenance Management

The study of the structural integrity of elements related to power generation, such as wind turbines or solar concentrator tubes, have gained great importance in recent days due to their importance from the reliability, availability, safety, and cost points of view. Maintenance of the elements related to power generation is crucial for the proper management of power generation plants and in order to avoid lost productivity. In the case of the wind turbines, this is more relevant when the offshore wind turbine is considered. This work introduces a novel design of a Fault Detection and Diagnosis (FDD) model based on guided waves and infrared thermography. The usefulness of UAVs is exposed to smart and automatically analyze renewable energy plants with the help of onboard sensor

A New Fault Location Approach for Acoustic Emission Techniques in Wind Turbines

The renewable energy industry is undergoing continuous improvement and development worldwide, wind energy being one of the most relevant renewable energies. This industry requires high levels of reliability, availability, maintainability and safety (RAMS) for wind turbines. The blades are critical components in wind turbines. The objective of this research work is focused on the fault detection and diagnosis (FDD) of the wind turbine blades. The FDD approach is composed of a robust condition monitoring system (CMS) and a novel signal processing method. CMS collects and analyses the data from different non-destructive tests based on acoustic emission. The acoustic emission signals are collected applying macro-fiber composite (MFC) sensors to detect and locate cracks on the surface of the blades. Three MFC sensors are set in a section of a wind turbine blade. The acoustic emission signals are generated by breaking a pencil lead in the blade surface.This method is used to simulate the acoustic emission due to a breakdown of the composite fibers. The breakdown generates a set of mechanical waves that are collected by the MFC sensors. A graphical method is employed to obtain a system of non-linear equations that will be used for locating the emission source. This work demonstrates that a fiber breakage in the wind turbine blade can be detected and located by using only three low cost sensors. It allows the detection of potential failures at an early stages, and it can also reduce corrective maintenance tasks and downtimes and increase the RAMS of the wind turbine