Network observability and bad data processing algorithm for distribution networks

A distribution automation system (DAS) aims for better management and control of the distribution networks. An efficient network observability, bad data detection and state estimation solution technique is a prerequisite for the success of DAS. This paper presents an efficient and robust three-phase state estimation (SE) algorithm for application to radial distribution networks. The extension of the method to the network observability analysis and bad data processing is discussed in detail. This method exploits the radial nature of the network and uses forward and backward propagation scheme to estimate the line flows, node voltages and loads at each node, based on the measured quantities. The SE cannot be executed without adequate number of measurements. The proposed method has been tested to analyze several practical distribution networks of various voltage levels and also having high R/X ratio of lines. The results for a typical network are presented for illustration purposes.