Fault Classification of Power Distribution Cables by Detecting Decaying DC Components with Magnetic Sensing

Abstract

Fault classification of power distribution cables is essential for tripping relays, pinpointing fault location, and repairing failures of a distribution network in the power system. However, existing fault-classification techniques are not totally satisfactory because they may (a) require the pre-calibration of responding threshold for each network, (b) fail to identify the three-phase short-circuit faults since some electrical parameters (e.g., phase angle) are still symmetrical even in abnormal status, and (c) be invulnerable of electromagnetic interferences. In this paper, a fault-classification technique by detecting decaying DC components of currents in faulted phases through magnetic sensing is proposed to overcome the shortcomings mentioned above. Firstly, the three-phase currents are reconstructed by magnetic sensing with a stochastic optimization algorithm, which avoids the waveform distortion in the measurement by current transformers that incurred by the DC bias. Then the DC component is extracted by mathematical morphology (MM) in phase currents to identify the fault type together with the polarity of DC components. This method was verified successfully for various fault types on a 22-kV power distribution cable in simulation and also a scaled power distribution network experimentally. The proposed method can enhance the reliability of the power distribution network and contribute to smart grid development.