122 research outputs found
Condition-based maintenance of wind turbine blades
The blades of offshore wind farms (OWTs) are susceptible to a wide variety of diverse sources of
damage. Internal impacts are caused primarily by structure deterioration, so even though outer
consequences are the consequence of harsh marine ecosystems. We examine condition-based
maintenance (CBM) for a multiblade OWT system that is exposed to environmental shocks in this
work. In recent years, there has been a significant rise in the number of wind turbines operating
offshore that make use of CBMs. The gearbox, generator, and drive train all have their own
vibration-based monitoring systems, which form most of their foundation. For the blades, drive
train, tower, and foundation, a cost analysis of the various widely viable CBM systems as well as
their individual prices has been done. The purpose of this article is to investigate the potential
benefits that may result from using these supplementary systems in the maintenance strategy.
Along with providing a theoretical foundation, this article reviews the previous research that has
been conducted on CBM of OWT blades. Utilizing the data collected from condition monitoring,
an artificial neural network is employed to provide predictions on the remaining life. For the
purpose of assessing and forecasting the cost and efficacy of CBM, a simple tool that is based on
artificial neural networks (ANN) has been developed. A CBM technique that is well-established
and is based on data from condition monitoring is used to reduce cost of maintenance. This can be
accomplished by reducing malfunctions, cutting down on service interruption, and reducing the
number of unnecessary maintenance works. In MATLAB, an ANN is used to research both the
failure replacement cost and the preventative maintenance cost. In addition to this, a technique for
optimization is carried out to gain the optimal threshold values. There is a significant opportunity
to save costs by improving how choices are made on maintenance to make the operations more
cost-effective. In this research, a technique to optimizing CBM program for elements whose
deterioration may be characterized according to the level of damage that it has sustained is
presented. The strategy may be used for maintenance that is based on inspections as well as
maintenance that is based on online condition monitoring systems
Maintenance Optimization and Inspection Planning of Wind Energy Assets: Models, Methods and Strategies
Designing cost-effective inspection and maintenance programmes for wind energy farms is a complex task involving a high degree of uncertainty due to diversity of assets and their corresponding damage mechanisms and failure modes, weather-dependent transport conditions, unpredictable spare parts demand, insufficient space or poor accessibility for maintenance and repair, limited availability of resources in terms of equipment and skilled manpower, etc. In recent years, maintenance optimization has attracted the attention of many researchers and practitioners from various sectors of the wind energy industry, including manufacturers, component suppliers, maintenance contractors and others. In this paper, we propose a conceptual classification framework for the available literature on maintenance policy optimization and inspection planning of wind energy systems and structures (turbines, foundations, power cables and electrical substations). The developed framework addresses a wide range of theoretical and practical issues, including the models, methods, and the strategies employed to optimise maintenance decisions and inspection procedures in wind farms. The literature published to date on the subject of this article is critically reviewed and several research gaps are identified. Moreover, the available studies are systematically classified using different criteria and some research directions of potential interest to operational researchers are highlighted
Maintenance models applied to wind turbines. A comprehensive overview
ProducciĂłn CientĂficaWind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models
Fault detection of a wind turbine generator bearing using interpretable machine learning
A wind turbine is subjected to a number of degradation mechanisms during its operational lifetime. If left unattended, the degradation of components will result in poor performance and potential failure. Hence, to mitigate the risk of failures, it is imperative that the wind turbines are regularly monitored, inspected, and optimally maintained. Offshore wind turbines are normally inspected and maintained at fixed intervals (generally six-month intervals) and the maintenance program (list of tasks) is prepared using experience or risk-based reliability analysis, like risk-based inspection (RBI) and reliability-centered maintenance (RCM). This time-based maintenance program can be improved by incorporating results from condition monitoring (CM) involving data acquisition using sensors and fault detection using data analytics. It is important to ensure quality and quantity of data and to use correct procedures for data interpretation for fault detection to properly carry out condition assessment. This thesis contains the work carried out to develop a machine learning (ML) based methodology for detecting faults in a wind turbine generator bearing. The methodology includes application of ML using supervisory control and data acquisition (SCADA) data for predicting the operating temperature of a healthy bearing, and then comparing the predicted bearing temperature with the actual bearing temperature. Consistent abnormal differences between predicted and actual temperatures may be attributed to the degradation and presence of a fault in the bearing. This fault detection can then be used for rescheduling the maintenance tasks. The methodology is discussed in detail using a case study. In this thesis, interpretable ML tools are used to identify faults in a wind turbine generator bearing. Furthermore, variables affecting the generator bearing temperature are investigated. The analysis used two years of operational data from a 2 MW offshore wind turbine located in the Gulf of Guinea off the west coast of Africa. Out of the four ML models that were evaluated, the XGBoost model was determined to be the most effective performer. After utilizing the Shapley additive explanations (SHAP) to analyze the XGBoost model, it was determined that the temperature in the generator phase windings had the most significant effect on the model's predictions. Finally, based upon the deviation between the actual and the predicted temperatures, an anomaly in the generator bearing was successfully identified two months prior to a generator failure occurring.Masteroppgave i havteknologiHTEK3995MAMN-HTEKMAMN-HTE
Impact of condition monitoring on the maintenance and economic viability of offshore wind turbines
This study explores how condition monitoring (CM) can help operate offshore wind turbines (OWTs) effectively and economically. In this paper, the Petri Net (PN) simulation models are developed to quantitatively assess the OWT availability and operation and maintenance (O&M) costs. By investigating the impact of two CM approaches (i.e. purpose-designed CM and Supervisory Control and Data Acquisition (SCADA)-based CM) and their combinations with various maintenance strategies, the paper addresses two fundamental questions about OWT CM that have plagued the offshore wind sector for many years. They are âis a wind farm SCADA system a viable alternative to purpose-designed condition monitoring system (CMS)â and âwhat is the best way to integrate CMSs and maintenance strategies to maximise the financial benefit of OWTsâ. The research suggests that although utilising both a wind farm SCADA system and a purpose-designed CMS can achieve the highest turbine availability, it is not the most cost-effective option in terms of maintenance expenses. Instead, combining purpose-designed CM with less frequent advanced service can achieve the desired availability at the lowest cost. Furthermore, the use of a purpose-designed CMS is essential for the economical operation of OWTs and cannot be replaced by the current wind farm SCADA system.</p
Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review
From MDPI via Jisc Publications RouterHistory: accepted 2021-09-14, pub-electronic 2021-09-20Publication status: PublishedModern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms
Maintenance Management of Wind Turbines
âMaintenance Management of Wind Turbinesâ considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements
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 operationâs 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
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