Online Condition Monitoring of MV Switchgear Using $D$ -Dot Sensor to Predict Arc-Faults

High energy arc faults in medium-voltage (MV) switchgear are serious hazards to personnel or equipment, and may cause process interruptions. Most of the electrical faults leading to arc are developed slowly, e.g., due to insulation degradation or bad connection. In this paper, the detection of partial discharges (PDs) and low energy arcing between loose contacts has been proposed for online monitoring of MV switchgear. The PD measurements in a switchgear panel and arcing measurements across a 0.2-mm sphere-to-rod gap have been carried out. Measured signals are captured by a differential electric field sensor (D -dot sensor) and recorded by a high-frequency oscilloscope. In general, online measured signals are suppressed by high-frequency noise, and therefore, de-noising of measurements is of paramount importance to get reliable information about a fault. An implementation of discrete wavelet transform, to de-noise the measured signals, has been proposed in this paper. Comparison with a well-known infinite impulse response filtering technique has been made. Time and frequency domain comparisons between original and de-noised signals reveal the significance of this technique for arc fault prediction in MV switchgear. A layout for the integration of online monitoring to central control is also presented.

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