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

Abstract

Interval state estimation (ISE) can estimate state intervals of power systems according to confidence intervals of predicted pseudo-measurements, thereby analyzing the impact of uncertain pseudo-measurements on states. However, predicted pseudo-measurements have prediction errors, and their confidence intervals do not necessarily contain the truth values, leading to estimation biases of the ISE. To solve this problem, this paper proposes a pseudo-measurement interval prediction framework based on the Gaussian process regression (GPR) model, thereby improving the prediction accuracy of pseudomeasurement confidence intervals. Besides, a weight assignment strategy for improving the robustness of weighted least squares (WLS) ISE is proposed. This strategy quantifies the deviation between the pseudo-measurement intervals and their estimated intervals and assigns smaller weights to the pseudomeasurement intervals with larger deviations, thereby improving the estimation accuracy and robustness of the ISE. This paper adopts the data from the supervisory control and data acquisition (SCADA) system of the New York Independent System Operator (NYISO). It verifies the advantages of the GPR method for pseudo-measurement interval prediction by comparing it with the quantile regression and neural network methods. In addition, this paper demonstrates the effectiveness of the proposed weight assignment strategy through the IEEE 14-bus case. Finally, the differences in the estimation accuracy and the bad data identification between the robust interval state estimation and deterministic state estimation are discussed.

A parallel approach for ultra-fast state estimation in large power system using graph partitioning theory

“…This mathematical construct encapsulates the intricacies of network connectivity, serving as a vital representation of interconnections. Inspired by effective methods such as those detailed in [15], the Laplacian matrix is methodically crafted. It hinges on the computation of diagonal entries within each row, representing the sum of non-diagonal entries, thus quantifying the interconnectedness of nodes.…”

“…Noteworthy exceptions include the application of spectral clustering for power system network partitioning during emergency conditions [13] and the use of the k-means algorithm to expedite load flow in [14]. In reference [15], a state estimation method based on Gaussian process regression is presented, utilizing data from the SCADA unit of the New York Independent System Operator. Reference [16] employs a combination of PMU and SCADA measurement units for state estimation.…”

“…This paper aims to contribute to the advancement of state estimation methodologies, particularly in the context of large-scale transmission networks. This paper builds upon the premise of spectral clustering introduced in [15] and harnesses the potential of linear SCADA-based state estimation described in [25], with the objective of achieving rapid and efficient parallel state estimation. Within each area, state estimation is conducted with precision, considering the local reference bus.…”