Title :
Fault Diagnosis for VSC-HVDC Using Wavelet Transform
Author :
Sun Xiaoyun ; Tong Xiangqian ; Yin Jun
Author_Institution :
Xi´an Univ. of Technol., Xi´an, China
Abstract :
VSC-HVDC is a kind of HVDC technology which based on voltage source converter and turn-off devices. Its poor over voltage/current capacity are prone to failure. Based on the established VSC-HVDC system simulation model, the DC voltage waveforms under various fault conditions are achieved, and then the character of system fault is decided according to the amplitude fluctuation range of DC voltage. Moreover, in full consideration of influence of transmission power, the wavelet analysis method is adopted to extract the feature of faulty signal, and combined with artificial neural network the system fault is identified. Simulation results show that this method can diagnose and identify VSC-HVDC fault effectively, and the accuracy is not impacted by transmission power.
Keywords :
HVDC power convertors; HVDC power transmission; digital simulation; failure analysis; fault diagnosis; feature extraction; neural nets; power engineering computing; wavelet transforms; DC voltage waveforms; VSC-HVDC system simulation model; artificial neural network; fault diagnosis; faulty signal; feature extraction; transmission power; turn-off devices; voltage source converter; voltage-current capacity; wavelet analysis method; wavelet transform; Artificial neural networks; Circuit faults; Fault diagnosis; Feature extraction; Power conversion; Vectors;
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2012 Asia-Pacific
Conference_Location :
Shanghai
Print_ISBN :
978-1-4577-0545-8
DOI :
10.1109/APPEEC.2012.6307632
