Analysis of variations in annual energy production based on types of suction side erosion at the blade tip of a wind turbine using numerical simulation

Lee, Keunseok; Im, Heejeon; Boo, Jeonghwan; Kim, Bum Suk

doi:10.1080/15435075.2023.2194972

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…Furthermore, the blades are easily damaged by the erosion of large solid particles of air, which directly affects the power output. Lee et al [21] analyzed the transient three-dimensional computational fluid dynamics of blade tip suction surface erosion shape and divided it into four grades. However, an accurate particle flow representation model is crucial to the analysis results.…”

Section: Aerodynamic 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: Aerodynamic Detectionmentioning

confidence: 99%