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

How to protect a wind turbine from lightning

Techniques for reducing the chances of lightning damage to wind turbines are discussed. The methods of providing a ground for a lightning strike are discussed. Then details are given on ways to protect electronic systems, generating and power equipment, blades, and mechanical components from direct and nearby lightning strikes

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

A machine learning approach to Structural Health Monitoring with a view towards wind turbines

The work of this thesis is centred around Structural Health Monitoring (SHM) and is divided into three main parts. The thesis starts by exploring di�erent architectures of auto-association. These are evaluated in order to demonstrate the ability of nonlinear auto-association of neural networks with one nonlinear hidden layer as it is of great interest in terms of reduced computational complexity. It is shown that linear PCA lacks performance for novelty detection. The novel key study which is revealed ampli�es that single hidden layer auto-associators are not performing in a similar fashion to PCA. The second part of this study concerns formulating pattern recognition algorithms for SHM purposes which could be used in the wind energy sector as SHM regarding this research �eld is still in an embryonic level compared to civil and aerospace engineering. The purpose of this part is to investigate the e�ectiveness and performance of such methods in structural damage detection. Experimental measurements such as high frequency responses functions (FRFs) were extracted from a 9m WT blade throughout a full-scale continuous fatigue test. A preliminary analysis of a model regression of virtual SCADA data from an o�shore wind farm is also proposed using Gaussian processes and neural network regression techniques. The third part of this work introduces robust multivariate statistical methods into SHM by inclusively revealing how the in uence of environmental and operational variation a�ects features that are sensitive to damage. The algorithms that are described are the Minimum Covariance Determinant Estimator (MCD) and the Minimum Volume Enclosing Ellipsoid (MVEE). These robust outlier methods are inclusive and in turn there is no need to pre-determine an undamaged condition data set, o�ering an important advantage over other multivariate methodologies. Two real life experimental applications to the Z24 bridge and to an aircraft wing are analysed. Furthermore, with the usage of the robust measures, the data variable correlation reveals linear or nonlinear connections

Condition Monitoring and Fault Detection of Blade Damage in Small Wind Turbines Using Time-series and Frequency Analyses

Condition monitoring systems are critical for autonomous detection of damage when operating remote wind turbines. These systems continually monitor the turbine’s operating parameters and detect damage before the turbine fails. Although common in utility-scale turbines, these systems are mostly undeveloped in distributed, small-scale turbines due to their high cost and need for specialized equipment. The Cal Poly Wind Power Research Center is developing a low-cost, modular solution known as the LifeLine system. The previous version contained monitoring equipment, but lacked decision-making capabilities. The present work builds on the LifeLine by developing software-based detection of blade damage. Detection is done by monitoring of tower vibrations, rotor speed, and generator power output. First, testing is completed to inform algorithm design: the tower vibrational response is recorded, and blade damage is simulated by adding a mass imbalance to one blade. From these results, several algorithms are developed, and their performance is analyzed in a cross-validation study. The time-series method known as the Nonlinear State Estimation Technique and Sequential Probability Ratio Test (NSET+SPRT) is implemented first. This algorithm is highly successful, with a 93.3% rate of correct damage detection; however, it occasionally raises false alarms during normal operation. A custom-built algorithm known as the Adaptive Fast Fourier Transform (AFFT) is also built; its strength lies in its elimination of false alarms. The final system utilizes a joint monitoring approach, combining the benefits of the NSET+SPRT and AFFT. The final algorithm is successful, correctly categorizing 95.5% of data when operating above 120RPM, and raising no false alarms in normal operation. This version is then implemented for live monitoring on the Cal Poly Wind Turbine, allowing for robust and autonomous detection of blade damage

Detection, Diagnosis and Prognosis: Contribution to the energy challenge: Proceedings of the Meeting of the Mechanical Failures Prevention Group

The contribution of failure detection, diagnosis and prognosis to the energy challenge is discussed. Areas of special emphasis included energy management, techniques for failure detection in energy related systems, improved prognostic techniques for energy related systems and opportunities for detection, diagnosis and prognosis in the energy field

Advanced Algorithms for Automatic Wind Turbine Condition Monitoring

Reliable and efficient condition monitoring (CM) techniques play a crucial role in minimising wind turbine (WT) operations and maintenance (O&M) costs for a competitive development of wind energy, especially offshore. Although all new turbines are now fitted with some form of condition monitoring system (CMS), very few operators make use of the available monitoring information for maintenance purposes because of the volume and the complexity of the data. This Thesis is concerned with the development of advanced automatic fault detection techniques so that high on-line diagnostic accuracy for important WT drive train mechanical and electrical CM signals is achieved. Experimental work on small scale WT test rigs is described. Seeded fault tests were performed to investigate gear tooth damage, rotor electrical asymmetry and generator bearing failures. Test rig data were processed by using commercial WT CMSs. Based on the experimental evidence, three algorithms were proposed to aid in the automatic damage detection and diagnosis during WT non-stationary load and speed operating conditions. Uncertainty involved in analysing CM signals with field fitted equipment was reduced, and enhanced detection sensitivity was achieved, by identifying and collating characteristic fault frequencies in CM signals which could be tracked as the WT speed varies. The performance of the gearbox algorithm was validated against datasets of a full-size WT gearbox, that had sustained gear damage, from the National Renewable Energy Laboratory (NREL) WT Gearbox Condition Monitoring Round Robin project. The fault detection sensitivity of the proposed algorithms was assessed and quantified leading to conclusions about their applicability to operating WTs