Development of an Improved LMD Method for the Low-Frequency Elements Extraction from Turbine Noise Background

Lida Liao, Bin Huang, Qi Tan, Kan Huang, Mei Ma and Kang Zhang
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Lida Liao: School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China
Bin Huang: School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China
Qi Tan: School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China
Kan Huang: School of Civil Engineering, Changsha University of Science & Technology, Changsha 410114, China
Mei Ma: School of Electric and Information Engineering, Yangzhou Polytechnic Institute, Yangzhou 225002, China
Kang Zhang: School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China

Energies, 2020, vol. 13, issue 4, 1-17

Abstract: Given the prejudicial environmental effects of fossil-fuel based energy production, renewable energy sources can contribute significantly to the sustainability of human society. As a clean, cost effective and inexhaustible renewable energy source, wind energy harvesting has found a wide application to replace conventional energy productions. However, concerns have been raised over the noise generated by turbine operating, which is helpful in fault diagnose but primarily identified for its adverse effects on the local ecosystems. Therefore, noise monitoring and separation is essential in wind turbine deployment. Recent developments in condition monitoring provide a solution for turbine noise and vibration analysis. However, the major component, aerodynamic noise is often distorted in modulation, which consequently affects the condition monitoring. This study is conducted to explore a novel approach to extract low-frequency elements from the aerodynamic noise background, and to improve the efficiency of online monitoring. A framework built on the spline envelope method and improved local mean decomposition has been developed for low-frequency noise extraction, and a case study with real near-field noises generated by a mountain-located wind turbine was employed to validate the proposed approach. Results indicate successful extractions with high resolution and efficiency. Findings of this research are also expected to further support the fault diagnosis and the improvement in condition monitoring of turbine systems.

Keywords: wind turbine; low-frequency noise; local mean decomposition; turbine noise; condition monitoring (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: 2020
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