Fake measurement identification in power substations based on correlation between data and distance of the evidence

In this study, a modified fake measurement identification approach is proposed. The accuracy of real-time data plays a dominant role in power system security and stability. Hence, different methods have been proposed to diagnose bad data. The existing fake measurement identification method is an excellent method which uses abundant data in digital substations to diagnose fake measurement and lock the protective trip when protective relay is likely to malfunction because of the bad data. However, the existing method ignores the correlation between current transformers (CTs)/voltage transformers (VTs) which may result in low probability or uncertain conditions especially in the power systems with low fault currents. A modified method is proposed based on the distance of the evidence and the correlation between data which noticeably omits the negative impact of bad data and provides stronger decisions, compared with the existing method. It can also specify the CTs/VTs providing bad data, by assigning low correlation factor.