Efficient Fault Feature Extraction and Fault Isolation for High Voltage DC Transmissions
High Voltage DC Transmission (HVDC) plays vital role in transmission and supply of high quality electric energy. The condition monitoring and protection are crucial for the normal operation of the HVDC system. Unfortunately, it is difficult to extract failure features and isolate the faults of HVDC because the transmission line always spreads a long distance. To address this issue, this work presents an efficient condition monitoring and fault diagnosis method for HVDC based on an independent component analysis (ICA)-wavelet feature extractor and a neural network fault classifier. The innovation of the proposed method lies that it appropriately introduced the ICA-wavelet to realize accurate fault feature extraction and the actual engineering data in Guangzhou HVDC system has been used to verify the effectiveness of the proposed method. The experimental results show that the new method can efficiently extract important fault features with heavy noise components depressed and the fault diagnosis rate reached to 83.3 %. Moreover, the proposed method is superior to the traditional methods. The findings of this work could provide valuable experience and data support for the construction and development of HVDC system in practice.
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