A New Fault Location Approach for Acoustic Emission Techniques in Wind Turbines

Carlos Quiterio Gómez Muñoz and Fausto Pedro García Márquez
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Carlos Quiterio Gómez Muñoz: Ingenium Research Group, Department of Business Management, University of Castilla-La Mancha, Ciudad Real 13071, Spain
Fausto Pedro García Márquez: Ingenium Research Group, Department of Business Management, University of Castilla-La Mancha, Ciudad Real 13071, Spain

Energies, 2016, vol. 9, issue 1, 1-14

Abstract: The renewable energy industry is undergoing continuous improvement and development worldwide, wind energy being one of the most relevant renewable energies. This industry requires high levels of reliability, availability, maintainability and safety (RAMS) for wind turbines. The blades are critical components in wind turbines. The objective of this research work is focused on the fault detection and diagnosis (FDD) of the wind turbine blades. The FDD approach is composed of a robust condition monitoring system (CMS) and a novel signal processing method. CMS collects and analyses the data from different non-destructive tests based on acoustic emission. The acoustic emission signals are collected applying macro-fiber composite (MFC) sensors to detect and locate cracks on the surface of the blades. Three MFC sensors are set in a section of a wind turbine blade. The acoustic emission signals are generated by breaking a pencil lead in the blade surface. This method is used to simulate the acoustic emission due to a breakdown of the composite fibers. The breakdown generates a set of mechanical waves that are collected by the MFC sensors. A graphical method is employed to obtain a system of non-linear equations that will be used for locating the emission source. This work demonstrates that a fiber breakage in the wind turbine blade can be detected and located by using only three low cost sensors. It allows the detection of potential failures at an early stages, and it can also reduce corrective maintenance tasks and downtimes and increase the RAMS of the wind turbine.

Keywords: acoustic emission; wind turbine; fault detection and diagnosis; macro-fiber composite; non-destructive testing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
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