Generalized Loss Based Geometric Unscented Kalman Filter for Robust Power System Forecasting-Aided State Estimation

Chen, Shanmou; Lin, Dongyuan; Wang, Shiyuan

doi:10.1109/lsp.2022.3221852

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…Furthermore, the proposed method is compared with some typical state estimators, such as WLS, GM, MS, LAV and some effective distributed robust state estimators, such as distributed MLE (DMLE) and DMEAV, which can be found in [10,13,31,32], respectively. In this paper, we set b i as 2.5, c i as 0.001, κ i as −10, τ as 0.55, α and β are set to 1.0 × 10 −6 according to [32,41], respectively. Notice that the scale parameter τ is designed based on the convergence theory presented in section 3.3 and the exact partitioned type of power systems.…”

Section: Simulation Resultsmentioning

confidence: 99%

A distributed multi-area power system state estimation method based on generalized loss function

Chen

Liu

et al. 2023

Meas. Sci. Technol.

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For power system state estimation, the measurement noise is usually assumed to follow the Gaussian distribution and the widely used estimator is the weighted least squares (WLS). However, the Gaussian distribution assumption is not always true and the performance of WLS becomes bad when the measurement noise is non-Gaussian. In this paper, a new distributed state estimation (SE) method is proposed for multi-area power systems. The proposed distributed method is based on the generalized loss function so that it can reduce the influence of non-Gaussian noise. Further, thanks to the matrix-splitting technology, the proposed distributed method can be implemented in a distributed way so that the computation time in each local area can be reduced. The simulation results carried out in the IEEE 30-bus and 118-bus systems verify the robustness and effectiveness of the proposed distributed SE method.

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