Linear Programming Contractor for Interval Distribution State Estimation Using RDM Arithmetic

Ngo, VietCuong; Wu, Wenchuan

doi:10.1109/tpwrs.2020.3033065

Cited by 11 publications

(4 citation statements)

References 28 publications

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“…3) Interval Estimation-Based Methods. In order to account for the uncertainties of measurements, network parameters, and DERs, interval state estimation (ISE) models are proposed in [220]- [224]. They differ from the classical deterministic SE models in that the outputs of ISE are interval values indicating the "boundary" of states.…”

Section: Handling Uncertainties and Missing/delayed/bad Datamentioning

confidence: 99%

“…In [223], the maximum and minimum values of states are estimated by solving an optimization problem with inequality constraints indicating the boundaries of estimated measurements derived from the measurement uncertainties. In [224], an ISE model based on the relative distance measure (RDM) arithmetic is proposed, which can provide more accurate estimated states and ensure the credibility of solutions.…”

Section: Handling Uncertainties and Missing/delayed/bad Datamentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Power System State Estimation Using Multiple Data Sources: PMUs, SCADA, AMI, and Beyond

Lin¹,

Abur²,

Gómez-Expósito³

et al. 2024

IEEE Trans. Smart Grid

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State estimation (SE) is indispensable for the situational awareness of power systems. Conventional SE is fed by measurements collected from the supervisory control and data acquisition (SCADA) system. In recent years, available data sources have been greatly enriched with the deployment of phasor measurement units (PMUs), advanced metering infrastructure (AMI), intelligent electronic devices (IEDs), etc. The integration of multiple data sources provides unprecedented opportunities for enhancing the performance of SE, but also presents major challenges to resolve, including optimal multi-type-sensor co-placement, multiple reporting rates and asynchronization, diverse types of measured quantities, correlations between measurements, integration of online and historical data sources, and system and measurement uncertainties. This paper outlines the state of the art and research opportunities in this area by providing a comprehensive literature review and extensive discussions. It starts by presenting the motivations and challenges, followed by a summary of existing data sources for SE in power systems. Subsequently, for both transmission system (static and dynamic) and distribution system SE, existing methods are systematically reviewed and categorized based on the addressed challenges. Interesting attempts of using novel measurements in SE are also studied. Finally, the paper concludes by providing a detailed discussion on the remaining research gaps and future research directions to be explored.

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Section: Handling Uncertainties and Missing/delayed/bad Datamentioning

confidence: 99%

Section: Handling Uncertainties and Missing/delayed/bad Datamentioning

confidence: 99%

A Survey of Power System State Estimation Using Multiple Data Sources: PMUs, SCADA, AMI, and Beyond

Lin¹,

Abur²,

Gómez-Expósito³

et al. 2024

IEEE Trans. Smart Grid

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some studies have focused on the model framework and solution algorithm of ISE considering different uncertain factors. In [11], pseudomeasurement uncertainties were considered and the relative distance measurement algorithm was proposed to obtain more credible estimated intervals. In [12], power fluctuations and measurement errors were considered and a modeling strategy and the estimation framework based on interval analysis were proposed to obtain an intuitive and effective system state boundary.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Yan,

Huo

et al. 2024

Journal of Modern Power Systems and Clean Energy

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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.

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“…In (Huang et al, 2019), an optimization model of interval SE is combined with bad data identification to enhance the robustness of interval SE. In (Ngo and Wu, 2021), nonlinear measurement equation is transformed into dual inequality linear equations by mean value theorem to ensure the reliability of estimated intervals. However, the current ISE methods focus on interval static SE and there is little research on interval dynamic state estimator (IDSE).…”

Section: Introductionmentioning

confidence: 99%

Improved unscented Kalman filter based interval dynamic state estimation of active distribution network considering uncertainty of photovoltaic and load

Lin²,

Wu³

et al. 2023

Front. Energy Res.

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State estimation of active distribution network (ADN) plays an important role in distribution energy management system. The increase penetration of distributed generations, especially the distributed photovoltaic (PV), in ADN leads to high uncertainty of ADN’s operation and the state of the ADN varies with the variation of the PV output power. For the uncertainty of PV power output, an interval dynamic state estimation (IDSE) method, which estimates the interval of ADN state variables is proposed in this paper. Firstly, considering the slow computation speed of the Unscented Kalman Filter (UKF), the square root UKF is used to predict the real-time operating level of the state variables. Secondly, since the power output of PV has features of the variation randomly, the neural network-based prediction intervals is employed to predict the power output interval of PV. Finally, the normal fluctuation range of ADN state is modelled as a bilevel non-linear programming problem to perform IDSE, which in turn monitors the operating state of ADN. The proposed method is evaluated on the IEEE 33-node and IEEE 123-node systems, respectively. The test results demonstrate that the dynamic status of the ADN can be tracked accurately using the proposed method.

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Linear Programming Contractor for Interval Distribution State Estimation Using RDM Arithmetic

Cited by 11 publications

References 28 publications

A Survey of Power System State Estimation Using Multiple Data Sources: PMUs, SCADA, AMI, and Beyond

A Survey of Power System State Estimation Using Multiple Data Sources: PMUs, SCADA, AMI, and Beyond

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

Improved unscented Kalman filter based interval dynamic state estimation of active distribution network considering uncertainty of photovoltaic and load

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