Abstract
Damaged wind turbine (WT) blades have an imbalanced load and abnormal vibration, which affects their safe and stable operation or even results in blade rupture. To solve this problem, this study proposes a new method to detect damage in WT blades using wavelet packet energy spectrum analysis and operational modal analysis. First, a wavelet packet transform is used to analyze the tip displacement of the blades to obtain the energy spectrum. The damage is detected preliminarily based on the energy change in different frequency bands. Subsequently, an operational modal analysis method is used to obtain the modal parameters of the blade sections and the damage is located based on the modal strain energy change ratio (MSECR). Finally, the professional WT simulation software GH (Garrad Hassan) Bladed is used to simulate the blade damage and the results are verified by developing an online fault diagnosis platform integrated with MATLAB. The results show that the proposed method is able to diagnose and locate the damage accurately and provide a basis for further research of online damage diagnosis for WT blades.
Highlights
Blades are the most important component of a wind turbine (WT) and their operating status is an important factor to ensure the normal and stable operation of WTs
The theoretical analysis shows that when the change in the stiffness matrix modal parameters before and after structural damage are used as input diagnostic information,and the the location parameters before and after structural damage are used as input diagnostic information, the location of the blade damage can be obtained using the modal strain energy change ratio (MSECR)
The proposed method for blade damage diagnosis and damage location based on the tip displacement signal, wavelet packet decomposition and operational modal analysis has the following characteristics: (1) The wind power simulation software Bladed was used to simulate the blade damage fault of a wind turbine by determining the change in the structural characteristic parameters before and after blade damage
Summary
Blades are the most important component of a wind turbine (WT) and their operating status is an important factor to ensure the normal and stable operation of WTs. In Reference [19], a neural network was used to diagnose blade damage; the vibration signal data were obtained from excitation experiments. These types of methods are more common than simulation methods using ANSYS but they are still in the experimental stage. In Reference [20], an exciter and several vibration sensors were installed on the blade of a Vestas V27 WT to monitor the operating conditions. Unlike acoustic emission technology and other methods of fault diagnosis that use sensors, an external signal excitation source does not have to be installed, which reduces the complexity and enhances the reliability of the system. The method can be applied to actual WTs by using a small number of patch-type vibration sensors on the blade tip
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