A case study of bad data detection for distribution feeder voltage measurement

Abstract

Voltage measurement data are obtained from sectionalizing switches with sensors that introduced for distribution automation. In future active distribution network, it will be very important to detect bad data that deteriorate measurement accuracy to utilize the data and for appropriate maintenance. State estimation using the measured data of distribution feeders has been studied. However, the bad data detection method has been mainly discussed for transmission networks. A known method for detecting bad data based on the weighted least square method is tested for voltage distribution estimation of feeder assuming that customer demand profile can be approximated by the normal distribution. Voltage distribution with smaller error can be obtained by discarding the bad data that are detected by the proposed method.