A Massively Parallel Framework for Very Large Scale Linear State Estimation

This paper presents a new computational framework for implementing a robust linear static state estimator that uses only synchronized phasor measurements. Robustness is achieved by an estimator minimizing the L1 norm of the measurement residuals. The proposed computational approach assumes the availability of an adequate number of processors which can run simultaneously. Multiple copies of the system will be created each having a different zone partitioning. A set of processors will be assigned to the zones of each system copy. Starting with the original zone partitioning of the system (based on geography or control areas), an automatic zone partitioning method is developed which will guarantee minimum required redundancy levels around every boundary bus in at least one copy of the system for bad data rejection. The proposed design makes it possible for the state estimators of every zone on every system copy to run in parallel using a multicore high-performance computer. This will make the overall computational time independent of the system size. The proposed estimator is successfully simulated using different power systems with 30, 140, and 2917 buses.