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

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 pseudo-measurement 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 pseudo-measurement 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 Pseudo-measurement Modelling Strategy for Active Distribution Networks Considering Uncertainty of DGs

Active distribution networks utilize ad-vanced sensors,communication,and control technologies to achieve flexible and intelligent power distribution management.Reliable state estimation(SE)is crucial for distribution management systems to monitor these net-works.Historically,the scarcity of measurement resources has hindered the application of SE technology in distribution networks.Establishing a dependable pseu-do-measurement model for active distribution networks can significantly enhance the feasibility of SE.This paper proposes a pseudo-measurement model that aligns with the actual operating status of the distribution network,considering the uncertainty in output from distributed generations(DGs)such as wind turbines and photovolta-ics.Firstly,it analyzes and models the uncertainty of high-penetration DG output,establishing a reliable out-put model that incorporates the physical characteristics of wind and photovoltaic output.Secondly,it proposes a pseudo-measurement modeling method based on support vector machine(SVM),where the kernel function of the SVM is weighted according to the information entropy of fluctuations in historical operating data.This weighting ensures that the established pseudo-measurement model better reflects the actual operating status of the active distribution network.Finally,a mathematical model for optimizing pseudo-measurement selection is developed,with the minimum state estimation error as the objective function and the observability of the active distribution network system as the constraint.Case studies demon-strate the accuracy and effectiveness of this approach.

Multi-area distributed three-phase state estimation for unbalanced active distribution networks

This paper proposes a new multi-area framework for unbalanced active distribution network(ADN) state estimation. Firstly, an innovative three-phase distributed generator(DG) model is presented to take the asymmetric characteristics of DG three-phase outputs into consideration. Then a feasible method to set pseudo-measurements for unmonitored DGs is introduced. The states of DGs,together with the states of alternating current(AC) buses in ADNs, were estimated by using the weighted least squares(WLS) method. After that, the ADN was divided into several independent subareas. Based on the augmented Lagrangian method, this work proposes a fully distributed three-phase state estimator for the multi-area ADN.Finally, from the simulation results on the modified IEEE123-bus system, the effectiveness and applicability of the proposed methodology have been investigated and discussed.