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

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.

Advanced analytics for detection and diagnosis of false alarms and faults: A real case study

“…The main contributions of this paper lie in the following aspects: This paper presents a new approach to determine the possibility of an error in the analysis of the data from SCADA system. There are many studies where fault prediction and diagnosis of WTs regarding the SCADA data are discussed, eg, based on the frequency of occurrence of the failures, using data mining algorithm to built time series models, studying the standard turbine performance parameters and the physics of the different failures, using graphical methods, or employing sensitivity analysis . However, these methods are not based on the multiple correlations that each parameter can have with rest of the parameter measured by the SCADA systems. The proposed methodology considers all the correlations between all the variables of the SCADA systems.…”

Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines

“…A proper CMS can be used to detect more faults. Early detection of incipient faults prevents major component failures and allows predictive strategies to be carried out [18][19][20]. The capability of a CMS depends on the number and type of sensors, and signal processing [21][22][23][24][25].…”

Correlated EEMD and Effective Feature Extraction for Both Periodic and Irregular Faults Diagnosis in Rotating Machinery

“…On the one hand, incipient faults would likely generate weak vibration energy, which could hardly cause any significant physical degradation within the solid structure, which makes it impossible for the vibration sensor to capture any useful pattern. On the other hand, acoustic sensor can cover a much more extensive range of frequencies which enables it to be embedded with much more extended types of fault signals [5,6]. However, the disadvantages accompanying the merits are the low signal to noise ratio (SNR) and low computational efficiency.…”