Soft Fault Location and Imaging Using Residual Voltage Inversion in Cable Networks

Abstract

Locations and parameters monitoring for soft faults in a cable network is of importance to prevent hard faults at an early stage and maintain the stability of the power system. The existing fault detection methods often identify faults using the fixed parameters in a reference cable model to locate the faults. However, the changes of the reference model by faults bring model mismatch, for example, the signal propagation speed is different in the line-like soft faults. The model mismatch will lead to inaccurate fault location and not to mention parameter imaging for faults. To this end, this article proposes a residual voltage inversion (RVI) method to learn the model of the cable network with unknown faults. RVI uses the residual voltages, that is, the difference between the scattering voltages measured at the ports and those generated by the current model, as the gradient to update the multiple parameter distributions of the cable network iteratively. The learned model can then be used to calculate the precise location, imaging of the length, capacitance, and resistance for line-like soft faults. The simulation results show that RVI locates the range of line-like soft faults with an accuracy over 90%, and achieves insulation layer and core conductor imaging with accuracies larger than 95% and 80%, respectively. In addition, the experimental tests are carried out to verify the feasibility and performance of RVI.