Bad data detection for smart grid state estimation

Abstract

Bad data detection and identification is an important step in state estimation procedures. Finding the values of the state variables relies on real time measurements which are normally contaminated by noise or may suffer some error due to misconfiguration. Furthermore, the data is a target for hackers who try to change some measurement readings that lead operators to take wrong decisions. The need for accurate and reliable measurements is one of the research areas that have been extensively investigated in the last few decades. in this paper, Multidimensional Scaling (MDS) is used as a new technique to identify the source of the bad data in the network. Weighted least square and Chi-squared test have been used to calculate the state variables and to test the presence of the bad data. Finally, MDS is used to identify the source of the bad data. Different scenarios have been tested on the IEEE 14 bus system by using the proposed method.