Least Absolute Value-Based Temperature-Dependent Robust State Estimation

Inaccuracies in both measurements and parameter determination can generate biased results in state estimation. The change in branch temperature precipitates fluctuations in resistance and branch flow measurements. Even when flow measurements derived from remote terminal units are precise, the erroneous resistance value can still render the estimation results inaccurate. In this paper, we propose a least absolute value based, temperature-dependent, static state estimation method. The proposed method is formulated as a linear programming, employing an L1 norm objective function and introducing a branch temperature with the expanded Jacobian to consider thermal inertia. The proposed estimator not only estimates the state but also estimates branch temperature along with the resistance. Therefore, our proposed method captures the accurate system states robust against the parameter error and measurement error. Furthermore, the proposed estimator can evaluate thermal inertia with higher accuracy compared to the existing method. The case studies illustrate the robustness of the proposed method against parameter and measurement inaccuracies. Additionally, it can estimate state and thermal inertia more effectively than the comparative estimator.