A review on fatigue damages in the wind turbines: Challenges in determining and reducing fatigue failures in wind turbine blades

R, Karthikeyan; Subbiah, Rajkumar; Ranganathan, Nalini; Bensingh, Joseph; Kader, Abdul; Nayak, Sanjay K.

doi:10.1177/0309524x19849851

Cited by 19 publications

(8 citation statements)

References 57 publications

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“…Although GFRP has the advantages of high strength, low mass, corrosion resistance, ease of fabrication, and low cost, weak interlaminar properties are a key factor affecting the lifetime of large wind turbine blades made with glass-fiber-reinforced composites [74,75]. Typical damage commonly observed in wind turbine blades is shown in Figure 4, for example, matrix cracking [76], delamination [77,78], fiber/matrix interface debonding [79], and fiber fracture [80]. Microcracks in the matrix are usually the first damage pattern exhibited by wind turbine blades.…”

Section: Wind Turbine Blade Damage Detectionmentioning

confidence: 99%

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Ding

Chao

Zhang

2023

Sensors

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Monitoring and maintaining the health of wind turbine blades has long been one of the challenges facing the global wind energy industry. Detecting damage to a wind turbine blade is important for planning blade repair, avoiding aggravated blade damage, and extending the sustainability of blade operation. This paper firstly introduces the existing wind turbine blade detection methods and reviews the research progress and trends of monitoring of wind turbine composite blades based on acoustic signals. Compared with other blade damage detection technologies, acoustic emission (AE) signal detection technology has the advantage of time lead. It presents the potential to detect leaf damage by detecting the presence of cracks and growth failures and can also be used to determine the location of leaf damage sources. The detection technology based on the blade aerodynamic noise signal has the potential of blade damage detection, as well as the advantages of convenient sensor installation and real-time and remote signal acquisition. Therefore, this paper focuses on the review and analysis of wind power blade structural integrity detection and damage source location technology based on acoustic signals, as well as the automatic detection and classification method of wind power blade failure mechanisms combined with machine learning algorithm. In addition to providing a reference for understanding wind power health detection methods based on AE signals and aerodynamic noise signals, this paper also points out the development trend and prospects of blade damage detection technology. It has important reference value for the practical application of non-destructive, remote, and real-time monitoring of wind power blades.

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Section: Wind Turbine Blade Damage Detectionmentioning

confidence: 99%

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Ding

Chao

Zhang

2023

Sensors

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“…Wind turbines are generally installed in locations with strong and durable wind fields [70]. While a large wind load drives their fast rotating for higher power generation, it also causes fatigue damage accumulation on the wind turbine components.…”

Section: Fatigue Reliability On Bladesmentioning

confidence: 99%

Fatigue reliability of wind turbines: historical perspectives, recent developments and future prospects

et al. 2022

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“…i is calculated automatically and different i represent different loop amplitudes. The ratio of fatigue cycles to total life failure cycles can be defined as damage factor d i [23]:…”

Section: Fatigue Load Modelingmentioning

confidence: 99%

A Machine Learning Method for Modeling Wind Farm Fatigue Load

2022

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Wake steering control can significantly improve the overall power production of wind farms. However, it also increases fatigue damage on downstream wind turbines. Therefore, optimizing fatigue loads in wake steering control has become a hot research topic. Accurately predicting farm fatigue loads has always been challenging. The current interpolation method for farm-level fatigue loads estimation is also known as the look-up table (LUT) method. However, the LUT method is less accurate because it is challenging to map the highly nonlinear characteristics of fatigue load. This paper proposes a machine-learning algorithm based on the Gaussian process (GP) to predict the farmlevel fatigue load under yaw misalignment. Firstly, a series of simulations with yaw misalignment were designed to obtain the original load data, which considered the wake interaction between turbines. Secondly, the rainflow counting and Palmgren miner rules were introduced to transfer the original load to damage equivalent load. Finally, the GP model trained by inputs and outputs predicts the fatigue load. GP has more accurate predictions because it is suitable for mapping the nonlinear between fatigue load and yaw misalignment. The case study shows that compared to LUT, the accuracy of GP improves by 17% (RMSE) and 0.6% (MAE) at the blade root edgewise moment and 51.87% (RMSE) and 1.78% (MAE) at the blade root flapwise moment.

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A review on fatigue damages in the wind turbines: Challenges in determining and reducing fatigue failures in wind turbine blades

Cited by 19 publications

References 57 publications

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Fatigue reliability of wind turbines: historical perspectives, recent developments and future prospects

A Machine Learning Method for Modeling Wind Farm Fatigue Load

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