Lp Quasi Norm State Estimator for Power Systems

Abstract

This paper proposes an L p (0<p<1) quasi norm state estimator for power system static state estimation. Compared with the existing L 1 and L 2 norm estimators, the proposed estimator can suppress the bad data more effectively. The robustness of the proposed estimator is discussed, and an analysis shows that its ability to suppress bad data increases as p decreases. Moreover, an algorithm is suggested to solve the nonconvex state estimation problem. By introducing a relaxation factor in the mathematical model of the proposed estimator, the algorithm can prevent the solution from converging to a local optimum as much as possible. Finally, simulations on a 3-bus DC system, the IEEE 14-bus and IEEE 300-bus systems as well as a 1204-bus provincial system verify the high computation efficiency and robustness of the proposed estimator.

Robust Interval State Estimation for Distribution Systems Considering Pseudo-measurement Interval Prediction

“…An important means to suppress the smearing effect is to reduce the weight of bad data by the weight assignment in the iterative process, thereby reducing the impact of bad data and improving the robustness. Weight assignment needs to follow a principle of assigning small weights to large residuals, which is a robust strategy commonly used in robust state estimation [34]. But for ISE, the "residuals" are not definite values but intervals, which need to be redefined.…”