Multivariable Analysis for Advanced Analytics of Wind Turbine Management

Operation and maintenance tasks on the wind turbines have an essen- tial role to ensure the correct condition of the system and to minimize losses and increase the productivity. The condition monitoring systems installed on the main components of the wind turbines provide information about the tasks that should be carried out over the time. A novel statistical methodology for multivariable analysis of big data from wind turbines is presented in this paper. The objective is to analyse the necessary information from the condition monitoring systems installed in wind farms. The novel approach filters the main parameters from the collected signals and uses advanced computational techniques for evaluating the data and giving mean- ing to them. The main advantage of the approach is the possibility of the big data analysis based on the main information available

A Critical Review on the Structural Health Monitoring Methods of the Composite Wind Turbine Blades

With increasing turbine size, monitoring of blades becomes increasingly im-portant, in order to prevent catastrophic damages and unnecessary mainte-nance, minimize the downtime and labor cost and improving the safety is-sues and reliability. The present work provides a review and classification of various structural health monitoring (SHM) methods as strain measurement utilizing optical fiber sensors and Fiber Bragg Gratings (FBG’s), active/ pas-sive acoustic emission method, vibration‒based method, thermal imaging method and ultrasonic methods, based on the recent investigations and prom-ising novel techniques. Since accuracy, comprehensiveness and cost-effectiveness are the fundamental parameters in selecting the SHM method, a systematically summarized investigation encompassing methods capabilities/ limitations and sensors types, is needed. Furthermore, the damages which are included in the present work are fiber breakage, matrix cracking, delamina-tion, fiber debonding, crack opening at leading/ trailing edge and ice accre-tion. Taking into account the types of the sensors relevant to different SHM methods, the advantages/ capabilities and disadvantages/ limitations of repre-sented methods are nominated and analyzed

A Condition Monitoring System for Blades of Wind Turbine Maintenance Management

Wind energy is one of the most competitive and efficient renewable energy. It requires an efficient management system to reduce costs, predict failures and increase the production. The main objective of this paper is to design the appropriate tests and develop a condition monitoring system (CMS) to display the surface temperature of any body state using infrared radiation. The data obtained from this system lead to identify the state of the surface. The CMS is used for maintenance management of wind turbines because it is necessary an effective system to display the surface temperature to reduce the energy losses. This paper analyses numerous scenarios and experiments on different surfaces in preparation for actual measurements of blade surfaces

Online Fault Detection in Solar Plants Using a Wireless Radiometer in Unmanned Aerial Vehicles

A novel Non-Destructive Test (NDT) is presented in this paper. It employs a radiometric sensor that measures the infrared emissivity of the solar panel surface embedded in an unmanned aerial vehicle. The measurements provided by the sensor will determine if the panel is healthy, damaged or dirty. A thermographic camera has been used to check the temperature variations and validate the results by the sensor. The study shows that the amount of dirt influences the temperature on the surface and the energy generated. Similarly, faults in photovoltaic cells influence the temperature of the panel. The NDT system is less expensive than traditional thermographic sensors or cameras. Early detection of these problems, together with an optimal maintenance strategy, allows to reduce costs and increase the competitiveness of this renewable energy source

SCADA and Artificial neural networks for maintenance management

Nowadays, the reliability of the wind turbines is essential to ensure the efficiency and the benefits of the wind energy. The SCADA system installed in a wind turbine generates lot of data that need to be processed. The information obtained from these data can be used for improving the operation and management, obtaining more reliable systems. The SCADA systems operate through different control rules that are predefined. However, a static control of the wind turbine can generate a miscorrelation between the control and the real conditions of the wind turbine. For example, two wind turbines can be separated several kilometers in the same wind farm, therefore, the operation conditions must be different and the control strategy should not be unique. This research work presents a method based on neural networks for a dynamic generation of the control strategy. The method suggests that the thresholds used for generating alarms can vary and, therefore, the control of the wind turbine will be adapted to each specific wind turbine

Artificial Intelligence for Concentrated Solar Plant Maintenance Management

Concentrated Solar Power (CSP) is an alternative to the conventional energy sources which has had significant advances nowadays. A proper predictive maintenance program for the absorber pipes is required to detect defects in the tubes at an early stage, in order to reduce corrective maintenance costs and increase the reliability, availability, and safety of the concentrator solar plant. This paper presents a novel approach based on signal processing employing neuronal network to determine effectively the temperature of pipe, using only ultrasonic transducers. The main novelty presented in this paper is to determine the temperature of CSP without requiring additional sensors. This is achieved by using existing ultrasonic transducers which is mainly designed for inspection of the absorber tubes. It can also identify suddenly changes in the temperature of the CSP, e.g. due to faults such as corrosion, which generate hot spots close to welds

A low steady HBsAg seroprevalence is associated with a low incidence of HBV-related liver cirrhosis and hepatocellular carcinoma in Mexico: a systematic review

To address the relationship between hepatitis B virus (HBV) endemicity and HBV-related liver diseases in Mexico. Research literature reporting on HBsAg and antibody to hepatitis B core antigen (anti-HBc) prevalence in Mexican study groups were searched in NLM Gateway, PubMed, IMBIOMED, and others. Weighted mean prevalence (WMP) was calculated from the results of each study group. A total of 50 studies were analyzed. Three nationwide surveys revealed an HBsAg seroprevalence of less than 0.3%. Horizontal transmission of HBV infection occurred mainly by sexual activity and exposure to both contaminated surgical equipment and body fluids. High-risk groups exposed to these factors included healthcare workers, pregnant women, female sex workers, hemodialysis patients, and emergency department attendees with an HBsAg WMP ranging from 1.05% (95% confidence interval [CI], 0.68–1.43) to 14.3% (95% CI, 9.5–19.1). A higher prevalence of anti-HBc in adults than those younger than 20 years was associated with the main risk factors. Anti-HBc WMP ranged from 3.13% (95% CI, 3.01–3.24) in blood donors to 27.7% (95% CI, 21.6–33.9) in hemodialysis patients. A heterogeneous distribution of HBV infection was detected, mainly in native Mexican groups with a high anti-HBc WMP of 42.0% (95% CI, 39.5–44.3) but with a low HBsAg WMP of 2.9% (95% CI 2.08–3.75). Estimations of the Mexican population growth rate and main risk factors suggest that HBsAg seroprevalence has remained steady since 1974. A low HBsAg prevalence is related to the low incidence of HBV-related liver cirrhosis and hepatocellular carcinoma (HCC) previously reported in Mexico

Rising rural body-mass index is the main driver of the global obesity epidemic in adults

Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities(.)(1,2) This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity(3-6). Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017-and more than 80% in some low- and middle-income regions-was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing-and in some countries reversal-of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories