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Partial discharge (PD) diagnostic is a crucial tool for condition monitoring of power system equipment (e.g. switchgear, cable) in the medium voltage (MV) network, which is degraded by the gradual deterioration of insulation elements, ageing, and various operational and environmental stresses. In the MV network, different types of PD faults are generated from different sources and to know the impact of an individual PD fault on the health of MV equipment, classification plays an important role. This paper aims to provide suitable techniques for classifying PD faults. The data is collected from an experimental investigation of three different types of PD faults from MV switchgear and classified using features extraction, dimensionality reduction and clustering techniques. To identify the best classification technique, dimensionality reduction techniques (principal component analysis and t-distributed stochastic neighbour embedding) are used, and their results are compared using the confusion matrix after applying k-means clustering technique.

Partial Discharge (PD) measurements are considered as the early detection of insulation degradation in power system equipment. In switchgear and power cables, multiple PD faults may exist, which make the detection and location of such incipient faults challenging. This paper deals with identification of multiple PD faults by a hybrid detection technique, by combining conventional and unconventional measurement methods.Unconventional PD measurements rely on detection of physical emissions due to such faults, e.g. detection of radio frequency (RF) electromagnetic (EM) fields in the vicinity of PD activity, ultrasonic waves, optical emissions and heat produced by the PD, whereas the conventional measurement methods are based on detection of high frequency current and voltage, superimposed on the power frequency current and voltage.In this paper, the apparent charge of PD events is calculated by using conventional measurement technique and EM signal energy is calculated based on unconventional method. The high frequency electric field was measured using a D-dot sensor. The comparison between the two parameters show that a second degree polynomial relation exists between the EM energy and apparent charge. The scatter plots between the two variables show a number of patterns due to the number of PD faults. Therefore, the detection of multiple faults is possible.

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.

Due to the wide dependency on electricity in our daily life, it is highly desired to have an uninterrupted and reliable supply of electricity for domestic, commercial, transportation, industrial and healthcare purposes. An arc fault occurring in switchgear causes long duration power interruption, damage to the equipment and hazard to personnel. Several arc protection systems have been introduced during last decade, mostly relying on reactive protection techniques. This paper introduces some unique methods detecting incipient faults leading to arc-flash in medium voltage (MV) and low voltage (LV) switchgear and motor controlgear. In MV switchgear, low power arcing and insulation degradation can be detected by differential electric field sensor (D-dot) and Rogowski coil (RC), and thermal degradation is detected by thermal ionization detector (TID) in LV switchgear. On-site testing is included in this paper to validate the laboratory test results.