A statistical-based criterion for incipient fault detection in underground power cables established on voltage waveform characteristics

The incipient faults which mainly occur due to the electric arc occurrence in the power cables with insulation defects are hardly detectable by the conventional protective relays, and over time can develop into a permanent fault in the system. Employing Kalman filter, this paper puts forward a method to detect the incipient faults and to discriminate them from other similar incidents in the power system. The proposed method is established on the comparison between the waveform of the measured voltage and fundamental component of the measured voltage, estimated by Kalman filter algorithm in the sending end of the cable during the fault. Employing the difference between the measured and estimated waveforms, the incipient fault detection and discrimination are carried out within two stages. In the first stage, event detection is relegalized by comparing the standard deviation of the obtained error with a certain threshold. The second stage is conducted to find the incipient fault based on the non-attenuating characteristic and quasi-periodic nature of the incipient fault. The feasibility of the proposed method is verified through computer simulation using four different electric arc models and also the acquired experimental data from real incipient faults.

Automated summary

This paper proposes a method to detect and discriminate incipient faults in power systems by comparing the waveform of measured voltage with the estimated fundamental component using a Kalman filter algorithm. The method involves two stages, where event detection is carried out by comparing standard deviations of errors in the first stage, and incipient fault identification is conducted based on the non-attenuating characteristic and quasi-periodic nature of the faults in the second stage. The feasibility of the proposed method is verified through computer simulations with different electric arc models and real experimental data.