Fault diagnosis in bipolar HVDC systems based on traveling wave theory by monitoring data from one terminal

This paper proposes a method for detecting, classifying and locating incident faults on transmission line (TL) of a bipolar HVDC system. This method is based on the traveling waves theory and uses the redundant discrete wavelet transform to filter voltage signals monitored at only one system terminal. The model represents the Brazilian HVDC system of the Madeira River, which is simulated in the Alternative Transient Program (ATP). Different fault scenarios are analyzed, being generated by varying type, resistance and fault location. Moreover, in order to make the simulation more realistic, the TL is modeled based on frequency dependent parameters. Thereby, the impacts of line parameter uncertainties and transient attenuation on the proposed method are analyzed. From the obtained results, it is concluded that the method correctly detects all fault types through the use of a self-adaptive detection threshold. Furthermore, it is verified that the self-adaptive threshold detects the fault times instants with an efficiency superior to the fixed threshold. In the classification step, all faults were correctly classified through rules elaborated based on the voltage variation level. Finally, the total average errors of fault location represents only 0.047% of the TL extension. Moreover, it is verified that fault location errors for pole:pole faults were inferior to those obtained for pole:ground faults, being the efficiency of the method inversely proportional to the fault resistance. •Traveling wave-based methods have been taken as suitable solutions for HVDC systems.•Techniques regarding the complete fault diagnosis are scarce in the literature.•A self-adaptive fault detection threshold based on wavelet transform is proposed.•Fault location and classification based on data monitored at one system terminal.•Influence of the fault characteristics on the method efficiency.