Distribution line parameter estimation driven by probabilistic data fusion of D‐PMU and AMI

Xiao, Mengmeng; Xie, Wei; Fang, Chen; Wang, Shaorong; Li, Yan; Liu, Shu; Ullah, Zia; Zheng, Xuejun; Azimi, Reza

doi:10.1049/gtd2.12224

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…that allows computing the vector of the estimates x, where ˆwill indicate the estimated quantities. The weighing matrix is: (11) where Σ is the covariance matrix of the random vector and Σ e prior is the covariance matrix of prior errors. Σ can be derived through the law of propagation of uncertainty from the covariance matrix of the random errors Σ e as [29]…”

Section: A Background On Synchrophasor Estimation In the Presence Of ...mentioning

confidence: 99%

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Refined Modeling and Compensation of Current Transformers Behavior for Line Parameters Estimation Based on Synchronized Measurements

Laurano

Pegoraro

Sitzia

et al. 2023

IEEE Open J. Instrum. Meas.

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Nowadays, in modern management and control applications, line parameters need to be known more accurately than in the past to achieve a reliable operation of the distribution grids. Phasor Measurement Units (PMUs) may improve line parameters estimation processes, but the accuracy of the result is affected by all the elements of the PMU-based measurement chain, in particular by the instrument transformers. Current transformers (CTs) are nonlinear and therefore their behavior is not easily described: their models cannot be straightforwardly included in estimation problem. In this regard, the paper refines modeling and compensation of CT systematic errors in line parameters estimation processes, based on different methods to describe the transformer behavior under various operating conditions. As main result, the systematic errors of CTs are remarkably identified and mitigated. Moreover, estimation of shunt susceptance values is significantly improved.

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Section: A Background On Synchrophasor Estimation In the Presence Of ...mentioning

confidence: 99%

“…In fact, [8]- [10] only consider the error contribution due to PMUs. In [11], [12] the entire measurement chain is taken into account but assuming that errors are only random. In [13] the limit of neglecting the error contribution of instrument transformers (ITs) in the estimation model is discussed.…”

Section: Introductionmentioning

confidence: 99%

Refined Modeling and Compensation of Current Transformers Behavior for Line Parameters Estimation Based on Synchronized Measurements

Laurano

Pegoraro

Sitzia

et al. 2023

IEEE Open J. Instrum. Meas.

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show abstract

“…The method of data fusion can improve the accuracy of diagnosis. [16] fuses the data of Micro-PMU and SCADA, and proposes a distribution network state estimation algorithm based on the least squares method of dynamic variable weights to achieve high-precision state estimation, and the effectiveness of the algorithm is verified by tests; [17] A hybrid dynamic estimation algorithm for PMU and SCADA measurements is proposed, and experimental studies show that the hybrid method can improve the estimation to a certain extent; in [18], the slow sampling rate SCADA data and the high sampling rate PMU are fused to the dynamic state of the power system In the estimator, the dynamic tracking of the power system is realized, and the test results prove its availability; [19] uses the fusion method to make the PMU fill the missing SCADA data, and establish a multi-time scale data set for multi-time scale state estimation.…”

Section: Introductionmentioning

confidence: 99%

Power grid fault diagnosis method based on alarm information and PMU fusion

liu¹,

Zheng²,

Zhang³

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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With the rapid development of smart grid technology, more and more attention has been paid to the stability of the power grid. When the power grid fails, the dispatch center will receive various types of data, including the PMU data collected by the PMU (Power Management Unit) device used by the Wide Area Monitoring System (WAMS) and the data collected by the Supervisory Control System for Data Acquisition and Control (SCADA). Alarm information data, they describe the same grid fault from different dimensions. At present, the power grid fault diagnosis based on PMU data and alarm information texts has been fully studied, but the information source is single, and it is difficult to ensure the accuracy of the diagnosis results when complex faults and multiple faults occur in the power grid. This paper starts with the information source of power grid fault diagnosis and develops a power grid fault diagnosis method that integrates the dual data sources of PMU and alarm information: 1. effectively connect alarm message text and PMU based on fault events. 2. complete the fusion of PMU and alarm information to improve the scope and accuracy of power grid fault diagnosis. Finally, the simulation fault data is used to test. The experimental results show that the accuracy of fault diagnosis of dual data source fusion is higher than that of single data.

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A Levenberg–Marquardt algorithm‐based line parameters identification method for distribution network considering multisource measurement

Xu,

Song,

et al. 2024

IET Renewable Power Gen

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The uncertainty brought by the integration of distributed generations in distribution networks poses a higher demand for situation awareness in the distribution network. Accurate identification of distribution network line parameters is of great significance for the operation and control of the distribution network. This paper proposes a method for identifying distribution network line parameters considering multisource measurement. Firstly, the initial values of conductivity and susceptance are obtained through linear regression and converted into resistance and reactance, respectively. Then, based on the series parallel connection of the network end branches, a non‐linear function about resistance reactance is derived. By combining the measurement data of micro phasor measurement unit and advanced metering infrastructure at multiple times, the non‐linear measurement equation of the line is established, and the Levenberg–Marquardt algorithm is used to solve the non‐linear function, thus achieving the identification of distribution line parameters. The case study demonstrates the accuracy and effectiveness of the proposed method.

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Distribution line parameter estimation driven by probabilistic data fusion of D‐PMU and AMI

Cited by 8 publications

References 38 publications

Refined Modeling and Compensation of Current Transformers Behavior for Line Parameters Estimation Based on Synchronized Measurements

Refined Modeling and Compensation of Current Transformers Behavior for Line Parameters Estimation Based on Synchronized Measurements

Power grid fault diagnosis method based on alarm information and PMU fusion

A Levenberg–Marquardt algorithm‐based line parameters identification method for distribution network considering multisource measurement

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