Cost-Optimal Maintenance Planning for Defects on Wind Turbine Blades

Yang, Yi; Sørensen, John Dalsgaard

doi:10.3390/en12060998

Cited by 10 publications

(9 citation statements)

References 17 publications

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“…These methods are based on the use of considerable amounts of data to evaluate [144] Loss of Power Supply Probability, annual cost sensitivity [148] their statistical characteristics and application to the problem studied. Most common methods are based on three techniques: Bayesian analysis, where probability represents knowledge of the system instead of possibility of an event occurring [114,193,194]; Markov processes, in which the probability of an event happening depends on the previous state of the system [195,196]; and Monte Carlo simulations, based on random sampling to obtain results and solve problems [197][198][199]. The latter two are also found, but less used.…”

Section: Methodsmentioning

confidence: 99%

A Comprehensive Review of Artificial Intelligence and Wind Energy

Márquez

Gonzalo

2021

Arch Computat Methods Eng

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Support of artificial intelligence, renewable energy and sustainability is currently increasing through the main policies of developed countries, e.g., the White Paper of the European Union. Wind energy is one of the most important renewable sources, growing in both onshore and offshore types. This paper studies the most remarkable artificial intelligence techniques employed in wind turbines monitoring systems. The principal techniques are analysed individually and together: Artificial Neural Networks; Fuzzy Logic; Genetic Algorithms; Particle Swarm Optimization; Decision Making Techniques; and Statistical Methods. The main applications for wind turbines maintenance management are also analysed, e.g., economic, farm location, non-destructive testing, environmental conditions, schedules, operator decisions, power production, remaining useful life, etc. Finally, the paper discusses the main findings of the literature in the conclusions.

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Section: Methodsmentioning

confidence: 99%

A Comprehensive Review of Artificial Intelligence and Wind Energy

Márquez

Gonzalo

2021

Arch Computat Methods Eng

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“…Step 3: Calculate the weights: After the judgement matrix A is derived from the opinions of experts, numerical analysis is applied to calculate the maximum eigenvalue and its corresponding eigenvector, as shown in Equation (10). The relative importance or weight distribution of the factors may be sorted by analyzing the eigenvector.…”

Section: 468mentioning

confidence: 99%

Research on CBM of the Intelligent Substation SCADA System

Liu

Chou

et al. 2019

Energies

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An equipment status management and maintenance platform of an intelligent substation monitoring and control system is built in the Tai-Tam substation of the Taipower company. The real-time operating status of the equipment, such as the server, supervisory control and data acquisition (SCADA) human–machine interface (HMI) software, switches, intelligent electronic devices (IEDs), merging units (MUs), as well as the entire SCADA system, are evaluated comprehensively. First, the status information of all equipment is collected, and the theory of relative deterioration degree (RDD) and fuzzy theory (FT) are applied to calculate the fuzzy evaluation matrix of the equipment influencing factors. Then, the subjective analytic hierarchy process (AHP) and the objective entropy method for weighting are combined to calculate the comprehensive weights of the equipment influencing factors. Finally, the result of the equipment status evaluation is obtained using the fuzzy comprehensive evaluation (FCE) method and is presented at the equipment status management and maintenance platform. Such equipment status evaluation results can be used by the inspection and maintenance personnel to determine the priority for equipment maintenance and repair. The result of this study may serve as a valuable reference to utility companies when making maintenance plans.

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“…A discrete Markov chain model as a simplified probabilistic model for damages in wind turbine blades is introduced in [16]. The approach is based on a six-level damage categorization scheme applied by the wind industry, with the aim of providing decision makers with cost-optimal inspection intervals and maintenance strategies for the most common challenges that wind turbine blades face.…”

Section: Introductionmentioning

confidence: 99%

“…Figure16. Indicative allocation of piezoelectric sensors for detection of faults or damage with acoustic emission technique[93].…”

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confidence: 99%

A Comprehensive Analysis of Wind Turbine Blade Damage

2021

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The scope of this article is to review the potential causes that can lead to wind turbine blade failures, assess their significance to a turbine’s performance and secure operation and summarize the techniques proposed to prevent these failures and eliminate their consequences. Damage to wind turbine blades can be induced by lightning, fatigue loads, accumulation of icing on the blade surfaces and the exposure of blades to airborne particulates, causing so-called leading edge erosion. The above effects can lead to damage ranging from minor outer surface erosion to total destruction of the blade. All potential causes of damage to wind turbine blades strongly depend on the surrounding environment and climate conditions. Consequently, the selection of an installation site with favourable conditions is the most effective measure to minimize the possibility of blade damage. Otherwise, several techniques and methods have already been applied or are being developed to prevent blade damage, aiming to reduce damage risk if not able to eliminate it. The combined application of damage prevention strategies with a SCADA system is the optimal approach to adequate treatment.

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Cost-Optimal Maintenance Planning for Defects on Wind Turbine Blades

Cited by 10 publications

References 17 publications

A Comprehensive Review of Artificial Intelligence and Wind Energy

A Comprehensive Review of Artificial Intelligence and Wind Energy

Research on CBM of the Intelligent Substation SCADA System

A Comprehensive Analysis of Wind Turbine Blade Damage

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