1 research outputs found
Improving Power System State Estimation Based on Matrix-Level Cleaning
Power system state estimation is heavily subjected to measurement error,
which comes from the noise of measuring instruments, communication noise, and
some unclear randomness. Traditional weighted least square (WLS), as the most
universal state estimation method, attempts to minimize the residual between
measurements and the estimation of measured variables, but it is unable to
handle the measurement error. To solve this problem, based on random matrix
theory, this paper proposes a data-driven approach to clean measurement error
in matrix-level. Our method significantly reduces the negative effect of
measurement error, and conducts a two-stage state estimation scheme combined
with WLS. In this method, a Hermitian matrix is constructed to establish an
invertible relationship between the eigenvalues of measurements and their
covariance matrix. Random matrix tools, combined with an optimization scheme,
are used to clean measurement error by shrinking the eigenvalues of the
covariance matrix. With great robustness and generality, our approach is
particularly suitable for large interconnected power grids. Our method has been
numerically evaluated using different testing systems, multiple models of
measured noise and matrix size ratios.Comment: has been accepted by IEEE trans on Power System