Abstract
Blade imbalance fault caused by the marine organisms is considered as the most important fault in marine current turbines. Therefore, it is important to detect the fault accurately and quickly to mitigate its effect, minimize the downtime, and maximize the productivity. Imbalance fault detection methods using generator stator current signals have attracted attentions due to their low cost, operability and stability compared to the ones using vibration analysis. However, it is difficult to extract the fault signature and automatically detect the imbalance fault under different flow velocity conditions. In this paper, a wavelet threshold denoising-based imbalance fault detection method using the stator current is proposed. The signal is analyzed through three consecutive steps: the parameters offline setting based on wavelet threshold denoising, the Hilbert transform method and the Principle Component Analysis-based detection method. With this approach, the imbalance fault can be detected automatically. The imbalance fault detection is assessed under different flow velocity conditions and validated using an experimental platform. The results are promising with false alarm and false negative rates less than 1% and 5% respectively when using Q statistic. Moreover, the experimental results show that the proposed method has good stability under different flow velocity conditions.
Highlights
In recent years, with the increasing energy consumption around the world and the sharp increase of environmental pollution, marine energy has attracted more and more attention around the world due to its high energy density, predictability and relative stability [1]–[3]
The seabed environment is complex and marine current turbines (MCTs) installed under the sea will be considered as artificial reefs and attract a variety of marine organisms which will lead to imbalance faults [9], [10]
The proposed approach contains three parts: the parameters offline setting based on wavelet threshold denoising, the Hilbert transform (HT) method and the PCAbased detection method
Summary
With the increasing energy consumption around the world and the sharp increase of environmental pollution, marine energy has attracted more and more attention around the world due to its high energy density, predictability and relative stability [1]–[3]. As reported in [23], the wavelet transform method is used to preprocess the raw signal, reduce the interference components and extract the useful information to detect the fault. It is difficult for these methods using wavelet transform to set the appropriate parameters in practical application. A. PAPER CONTRIBUTIONS To reduce the interference and detect the imbalance fault under different flow velocity conditions, a wavelet threshold denoising-based imbalance fault detection method for MCTs is proposed. The proposed approach contains three parts: the parameters offline setting based on wavelet threshold denoising, the Hilbert transform (HT) method and the PCAbased detection method With this method, the imbalance fault of MCTs can be detected automatically.
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