Identification of Fault in HVDC Converters Using Wavelet Based Multi-Resolution Analysis

Fault identification and classification is very important for the secure and optimal exploitation of electric power systems. The wavelet analysis can be used as a tool for providing discriminative features with small dimensions to classify different disturbances in HVDC transmission system. This paper explores the application of wavelet based multi-resolution analysis (MRA) for signal decomposition to monitor some of the faults (e.g.- L-G fault, DC line fault, commutation failure) in the HVDC system. The faults in HVDC system can be classified by monitoring the signals both on AC and DC sides of the HVDC system like Inverter side AC phase currents, DC voltage, DC current, valve currents. The fault classifier can be developed from these monitored signals which show promising features to classify different disturbances in the HVDC System. The simulation results are also presented to verify the performance of the proposed method. The method has been used to classify different faults as well as to identify faulted phase(s) and valve(s) in case of AC faults and commutation failure respectively.