Prognostic techniques applied to maintenance of wind turbines: a concise and specific review

Leite, Gustavo de Novaes Pires; Araújo, Alex Maurício; Rosas, Pedro

doi:10.1016/j.rser.2017.06.002

Cited by 146 publications

(60 citation statements)

References 42 publications

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“…However, more advanced development of techniques and systems should be achieved in the future. On one hand, these existing techniques are not yet ready to be used and urgently need to be applied and proven in practice in order to allow their large-scale application in OWTs [201]. On the other hand, with the distance farther away from the mainland, it will be a significant act to establish the OWT maintenance information network and the remote IO&M systems to control and manage the offshore wind farms [15].…”

Section: Intelligent Operation and Maintenancementioning

confidence: 99%

Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development

et al. 2019

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With the depletion of fossil energy, offshore wind power has become an irreplaceable energy source for most countries in the world. In recent years, offshore wind power generation has presented the gradual development trend of larger capacity, taller towers, and longer blades. The more flexible towers and blades have led to the structural operational safety of the offshore wind turbine (OWT) receiving increasing worldwide attention. From this perspective, health monitoring systems and operational safety evaluation techniques of the offshore wind turbine structure, including the monitoring system category, data acquisition and transmission, feature information extraction and identification, safety evaluation and reliability analysis, and the intelligent operation and maintenance, were systematically investigated and summarized in this paper. Furthermore, a review of the current status, advantages, disadvantages, and the future development trend of existing systems and techniques was also carried out. Particularly, the offshore wind power industry will continue to develop into deep ocean areas in the next 30 years in China. Practical and reliable health monitoring systems and safety evaluation techniques are increasingly critical for offshore wind farms. Simultaneously, they have great significance for strengthening operation management, making efficient decisions, and reducing failure risks, and are also the key link in ensuring safe energy compositions and achieving energy development targets in China. The aims of this article are to inform more scholars and experts about the status of the health monitoring and safety evaluation of the offshore wind turbine structure, and to contribute toward improving the efficiency of the corresponding systems and techniques.

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Section: Intelligent Operation and Maintenancementioning

confidence: 99%

Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development

et al. 2019

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“…The main maintenance strategies are TPM, which is a methodology of continuous improvement based on what has been done by people [16][17][18][19][20]; Total Quality Maintenance (TQMain), whose philosophy is the continuous improvement of processes via empowerment of workers [3]; Lean Six Sigma (LSS) [21]; 5S [22]; E-maintenance [23]; and lean maintenance, which focuses on reducing the waste in any process, integrating the supply chain and increasing the value for the organization and the customers [24,25]. All these strategies are set within the Japanese philosophy of Total Quality Management (TQM) and are the basis of the tactical and operational strategy of the maintenance system [26,27].…”

Section: Types Of Models Applied At the Strategic And Tactical Levelsmentioning

confidence: 99%

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

et al. 2019

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Wind 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.

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“…Therefore, maintenance requirements and downtime will decrease, and the reliability of power generation will improve. Therefore, the final cost is kept as low as possible [5,6].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

Simani

Castaldi

2019

Applied Sciences

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Fault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines.

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Prognostic techniques applied to maintenance of wind turbines: a concise and specific review

Cited by 146 publications

References 42 publications

Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development

Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

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