Computationally Efficient Robust State Estimation for Power Transmission Systems with RTU and PMU Measurements

Finding a trade-off between robustness and computational efficiency is one of the major challenges when deriving a state estimation algorithm. Conventional Least Absolute Value (LAV) algorithms are robust against outliers, but impose significant computational burden. In this paper, we propose two LAV-based state estimators that are capable of taking into account conventional and synchrophasor measurements simultaneously. Both algorithms utilize a linear modelling framework for all available measurements. The first algorithm is based on the linear LAV method and is formulated as a single Linear Program, while the second leverages an alternative LAV-based formulation that can be solved by gradient-based methods. As a result, both algorithms are computationally significantly more efficient than the conventional LAV method, while preserving the bad data rejection properties. The performance of the algorithms is evaluated on a range of test cases of various sizes.