Murtaza Hashmi

Parameters Identification and Modeling of High-Frequency Current Transducer for Partial Discharge Measurements

Rogowski coil (RC) is a low-cost, air-cored, and flexible induction sensor for nonintrusive condition monitoring and thus can be used in a variety of applications. In this paper, a lumped parameter model of RC is presented and an experiment-based methodology is developed to determine its parameters. The performance of the RC is analyzed for detection and measurement of high-frequency (pulsed) signals such as partial discharge (PD) current pulses. A simple and efficient technique of numerical integration is adopted to avoid the conventional type of expensive and complex design analogue integrators. RC is modeled and simulated in the alternative transient program-electromagnetic transient program environment. The designed coil is tested to measure PDs in the laboratory. Simulated and experimental performance of RC is compared with a high-frequency current transformer. This comparison shows a good match and, hence, validates the design of RC for PD applications.

Signal processing of PD measurements to predict arcing faults in MV switchgears

Arcing faults in MV switchgear cause serious hazard to personnel, significant damage to equipment, and often serious process interruptions. Many of the faults develop slowly, e.g. because of insulation degradation or loose connection. An interesting research question is whether these developing faults could be detected before they escalate into devastating high-power faults. Detection of partial discharges (PD) or monitoring of temperature has been suggested in on-line monitoring systems. In this research, a switchgear panel has been subjected to PD in the laboratory and measurements have been captured by different sensors and recorded by high frequency oscilloscope. Generally, the on-line signals are suppressed by high frequency noise, therefore, the de-noising of PD measurement is of paramount importance to get reliable arcing fault prediction results. The discrete wavelet transform (DWT) to de-noise such PD signals has been employed in this paper. Time domain and frequency domain comparisons of original and de-noised PD signal reveal the significance of this technique for arcing fault prediction in medium voltage (MV) switchgears.

Application of passive filters to mitigate power quality problems caused by induction furnaces

The use of induction furnaces for steel production is increasing day by day. Induction furnaces produces power quality problem as result of their operation. This paper deals with modelling of induction furnaces in ETAP software with real data. Extent of power quality problem caused by the furnaces is identified. Elimination of the problem raised by induction furnaces is discussed by designing and implementing passive filters.

Intelligent fault diagnosis for on-line condition monitoring in smart distribution networks

Partial discharge (PD) measurements can be regarded as an effective and reliable tool for on-line condition monitoring and asset management of high voltage (HV) apparatus. Recently, a novel application is observed in the monitoring of falling trees on covered-conductor (CC) overhead distribution lines. In this paper, Rogowski and Pearson coils are used as sensors to detect PDs for this specific application. These sensors are non-intrusive and superior to the conventional PD detecting methods. In the next stage of future developments, the wired sensor will be converted into a wireless one. The challenges faced while implementing future wireless technology are also described here. In future, the wireless sensors will be integrated into distribution management system (DMS) to detect and localize the falling trees. The proposed intelligent fault diagnosis system will improve the safety of CC lines and make them more attractive to utilities due to reduced maintenance costs and visual inspection work. In addition, the reliability of the distribution system will improve which is one of the significant characteristics of the future smart distribution networks.