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

LIFECYCLE MANAGEMENT OF OFFSHORE AND ONSHORE WIND FARMS

Since the demand for renewable energy sources on a global scale has increased substantially over the past few years, a great number of businesses have made investments in this industry and are doing their utmost to make additional developments and discoveries in the field of wind energy. Wind farms, both onshore and offshore, are considered to be key contributors to the production of sustainable energy because they offer significant benefits, including a reduction in the negative effects on the environment and the production of harmful gases. The purpose of this thesis was to analyse and explore the lifecycle activities of a wind farm, in order to determine the failure modes, failure effects, and failure consequences of the critical components, as well as the strategies that can be used to counteract these failures. As many wind farms are reaching their end of life, so this thesis also focused on the techniques and strategies that can be done before dismantling, The thesis also includes a reliability analysis, failure mode effect and criticality analysis of generator and gearbox of wind turbine, and also lifecycle cost analysis of an offshore wind farm. These studies improved our knowledge of offshore windfarm operations, maintenance strategies and give a brief information about cost reduction techniques. Several steps from maintenance strategies based on NORSOK Z-008 and ISO-14224, as well as knowledge gained from research papers and interviews with engineers in moreld, were implemented to achieve the goals of this thesis

Classification of Wind Turbine Blade Performance State Through Statistical Methods

As wind turbines continue to age, wind farm operators face the challenge of optimizing maintenance scheduling to reduce the associated operation and maintenance (O&M) costs. Wind farm operators typically use conservative maintenance scheduling in order to maximize the uptime of their wind turbines. In most cases however, maintenance may not be necessary and the components could operate for longer before repairs are required. This work presents three papers that collectively focus on providing potentially useful information to aid wind farm operators in making maintenance decisions. In the first paper, the utilization of Geographic Information Systems (GIS) to illustrate data trends across wind farms is introduced to better understand an operation’s signature performance characteristics. It is followed by a paper that presents an improved condition monitoring system for the wind turbine blades through the use of the principal component analysis (PCA). The final paper introduces another condition monitoring system using a k-means clustering algorithm to determine the performance state of wind turbine blades

Feasibility study of Unmanned Aerial Vehicles (UAV) application for ultrasonic Non-Destructive Testing (NDT) of Wind Turbine Rotor Blades. Preliminary experiments of handheld and UAV utrasonic testing on glass fibre laminate

In this thesis, we have conducted a feasibility study on UAV application for ultrasonic pulsed non-destructive testing of wind turbine rotor blades. Due to the high initial cost of wind turbines, and their decreasing availability due to increasing size and offshore locations, it is imperative to properly maintain these structures over their 10-30-year lifetime. Operation and maintenance costs can account for 25-30% of the overall energy generation costs (MartinezLuengo, et al., 2016), where the wind turbine rotor blade can be considered the most critical component, accounting for 15-20% of the manufacturing costs. Thus, an increase in O&M efficiency of wind turbine rotor blades through condition monitoring can yield substantial financial benefits. Currently, Unmanned Aerial Vehicles (UAV) are in use for visual and thermography inspection of wind turbines. These techniques for structural condition monitoring does have serious limitations, as the condition of internal components in blades, built from glass fibre laminates, cannot be visually inspected. However, pulsed ultrasonic echo technique have proven highly efficient for wind turbine rotor blade inspection. The ultrasonic transducer requires surface contact with the examined material, and we investigated the potential of UAV implementation for fast, safe and reliable measurements of wind turbine rotor blades. This feasibility study investigates the applicability of ultrasonic testing of glass fibre laminates, specifically glass fibre produced by Lyngen Plast A/S. Firstly, we conducted handheld ultrasonic tests on simulated delamination defects, looking for damage indications on a voltage-time graph. Secondly, we induced damage on a 27mm thick sample through a 3-point bending test and measured the echo response from the ultrasonic pulse. The second experiment was repeated using a Storm AntiGravity UAV, producing promising results with preliminary instrumentation. A significant challenge to the feasibility of this study was the operational risks. We carried out a preliminary and qualitative risk assessment of the intended UAV operation by using the SWIFTanalysis and Bow-Tie method. The results were two important risk-mitigating measures. Risk reductive: “Design UAV for impact with wind turbine rotor blades,” and risk preventive: “Develop statistical data on wind conditions at wind turbine site, calculate low-risk dates for flight.” The implementation of the said measures, quality of our results, experiences from the UAV flight and concept considerations are presented throughout this paper. In the end, a conclusion is drawn and topics for future studies is presented

Identification of Surface Defects on Solar PV Panels and Wind Turbine Blades using Attention based Deep Learning Model

According to Global Electricity Review 2022, electricity generation from renewable energy sources has increased by 20% worldwide primarily due to more installation of large green power plants. Monitoring the renewable energy assets in those large power plants is still challenging as the assets are highly impacted by several environmental factors, resulting in issues like less power generation, malfunctioning, and degradation of asset life. Therefore, detecting the surface defects on the renewable energy assets would facilitate the process to maintain the safety and efficiency of the green power plants. An innovative detection framework is proposed to achieve an economical renewable energy asset surface monitoring system. First capture the asset's high-resolution images on a regular basis and inspect them to detect the damages. For inspection this paper presents a unified deep learning-based image inspection model which analyzes the captured images to identify the surface or structural damages on the various renewable energy assets in large power plants. We use the Vision Transformer (ViT), the latest developed deep-learning model in computer vision, to detect the damages on solar panels and wind turbine blades and classify the type of defect to suggest the preventive measures. With the ViT model, we have achieved above 97% accuracy for both the assets, which outperforms the benchmark classification models for the input images of varied modalities taken from publicly available sources