The mass of ice on wind turbines blades is one of the main problems that energy companies have in cold climates. This paper presents a novel approach to detect and classify ice thickness based on pattern recognition through guided ultrasonic waves and Machine Learning. To successfully achieve a supervised classification, it is necessary to employ a method that allows the correct extraction and selection of features of the ultrasonic signal. The main novelty in this work is that the approach considers four feature extraction methods to validate the results, grouped by linear (AutoRegressive (AR) and Principal Component Analysis) and nonlinear (nonlinear-AR eXogenous and Hierarchical Non-Linear Principal Component Analysis), and feature selection is done by Neighbourhood Component Analysis. A supervised classification was performed through Machine Learning with twenty classifiers such as Decision tree, Discriminant Analysis, Support Vector Machines, K-Nearest Neighbours, and Ensemble Classifiers. Finally, an evaluation of the classifiers was done in single frequency and multi-frequency modes, obtaining accurate results.

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.

a b s t r a c tParabolic 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.

Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers. Reliability Engineering & System Safety.

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.

India, Pakistan, and Bangladesh (IPB) are the largest South Asian countries in terms of land area, gross domestic product (GDP), and population. The growth in these countries is impacted by inadequate renewable energy policy and implementation over the years, resulting in slow progress towards human development and economic sustainability. These developing countries are blessed with huge potential for renewable energy resources; however, they still heavily rely on fossil fuels (93%). IPB is a major contributor to the total energy consumption of the world and its most energy-intensive building sector (India 47%, Pakistan 55% and Bangladesh 55%) displays inadequate energy performance. This paper comprehensively reviews the energy mix and consumption in IPB with special emphasis on current policies and its impact on economic and human development. The main performance indicators have been critically analyzed for the period 1970–2017. The strength of this paper is a broad overview on energy and development of energy integration in major South Asian countries. Furthermore, it presents a broad deepening on the main sector of energy consumption, i.e., the building sector. The paper also particularly analyzes the existing buildings energy efficiency codes and policies, with specific long-term recommendations to improve average energy consumption per person. The study also examines the technical and regulatory barriers and recommends specific measures to adapt renewable technologies, with special attention to policies affecting energy consumption. The analysis and results are general and can be applied to other developing countries of the world.

This paper presents anovelsignal processing approach that is able to automatically identify notches in pipelines in short distances. In addition, this method locates the geometric position of the notch and determines the size. The approach for fault detection and diagnosis presented look for as olution and then validates the solution by analyzing the signal which flows in the opposite direction. Micro Fiber Composite (MFC)t ransducers are used in an austenitic stainless steel pipeline, used in solar concentrators, in order to generate Ultrasonic Guided Waves. The main results presented in this paper can be summarized as: identification of edges or welds by multi-parametric analysis and comparison with the theoretical results predicted, notch location in the pipe by comparison of the position of echoes weighted with their amplitudes, and the flows izing of them by using attenuation curves of the echoes when theyp ropagate along the pipeline. This approach leads to employo nly one transmitter and one receptor for notch detection, location and diagnosis. The main advantage for the industry is the double check of presence of anotch with respect to other systems, which reduces false alarms during the inspections.

Icing blades require of advanced condition monitoring systems to reduce the failures and downtimes in Wind Turbine Blades (WTB). This paper presents a case study that combines ultrasonic techniques with Wavelet transforms for detecting ice on the blades. Lamb waves were generated with Macro Fibre Composites (MFC) and then were collected with MFC. Ice affects to the normal propagation of the wave through the material of the blade. The changes in the signal are due to the forces that ice exercise on the surface. Three different scenarios were considered: at room temperature; the frozen blade without accumulation of ice, and; the frozen blade with accumulation of ice on its surface. The novel approach can determine the state of the WTB. It leads a reduction of costs in inspection, downtime and the appearance of false alarms.