Comparison of state estimation algorithms for extreme contingencies

The electric power grid is a complex interconnected system that may be subjected to blackouts and external disasters. It is necessary for the utilities to restore their power system as quickly as possible during extreme conditions. The exact state of the electric grid is needed to perform any corrective or preventive action during such conditions. It is possible to get a reduced set of data or corrupted data during extreme contingencies for further analysis. Major loss of sensor data during extreme contingencies may not help to determine the exact state. State estimation (SE) helps to get a better picture of the power system with an available set of measurements. In the presence of phasor measurement unit (PMU) data, state estimation algorithm needs to be reformulated. The objective of this research is to identify the most efficient state estimation algorithm for different levels of data redundancy. This paper compares the performance of three state estimation algorithms with scattered and clustered loss of measurements.