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Smart Fault Detection For Low Voltage Distribution Systems
June 15 @ 11:30 am - 12:30 pm
Co-sponsored by: Nima Khoshsirat
While the penetration of renewable energy resources and the complexity of distribution networks are increasing, highly accurate protection schemes for Low Voltage (LV) distribution networks are not well developed yet. The accuracy of the conventional protection frameworks depends greatly on having an adequate number of measurement devices and adequate communication infrastructure installed. The high cost of monitoring devices motivates the distribution network operators to employ state estimators and forecasting models instead of increasing measurement and communication devices. In addition, the high intermittency in renewable power generation and unpredictable behaviour of the customer loads have originated from unknown changes in the dynamic behaviour of the distribution systems. This makes the conventional fault detectors with fixed current thresholds less accurate to detect faults, especially those with low fault currents. Hence, a predictive fault detection framework is required to predict the fault current thresholds for each time step.
This seminar shows how a Kalman filter as a forecasting-aided state estimator and quantile regression as a distribution free forecasting model can detect dynamic fault current thresholds in LV distribution networks. This presentation also shows that how dividing a distribution network into several protection zones with their own switches can locally isolate faults and keep the rest of the distribution network live. The performance of the Kalman filter and quantile regression as fault detection frameworks is evaluated by real distribution networks and customer data.
Speaker(s): Mr Mehdi Shafiei,
Bldg: s blcok
Room S620, Level 6, S block, Gardens Point Campus
Queensland University of Technology (QUT)