Identification of Faults in HVDC System using Wavelet Analysis

In present days high voltage direct current (HVDC) Transmission is most essential for bulk power transfer and large scale demands. Due to heavy power demand, maintenance of power quality has become very difficult. Most common disturbances are faults on the system which can be classified as symmetrical and non-symmetrical faults. The other disturbance like internal faults in converter and equipments also produce same effect. Some are very severe while some have less impact. Hence the identification and classification of faults is important for safe and optimal operation of power systems. For secure operation of a system a feasible approach is to monitor signals so that accurate and rapid classification of fault is possible for making correct protection control.To identify HVDC faults by using pure frequency or pure time domain based method is difficult. The pure frequency domain based methods are not suitable for time varying transients and the pure time domain based methods are very easily influenced by noise.Wavelet analysis is one of the methods used 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 faults in HVDC system. The faults in HVDC system can be classified by monitoring the signals both on AC and DC sides of the HVDC system. The fault classifier can be developed from these monitored signals which show promising features to classify different disturbances in the HVDC system. The simulations are carried out in MATLAB as computational engine. The results show that wavelet technique leads to a new way for fault detection and protection in HVDC system.