Variance analysis of robust state estimation in power system using influence function

Ho, Weng Khuen; Chen, Tengpeng; Ling, Keck Voon; Sun, Lu

doi:10.1016/j.ijepes.2017.04.009

Cited by 19 publications

(19 citation statements)

References 24 publications

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“…The M -estimation based methods are effective in the presence of outliers [14], [15]. But they can hardly be implemented in a recursive manner [16], thus generally suffer from computational load problem because of batch implementation. However, among all the M -estimators the Least Absolute Value (LAV) estimator can be made computationally efficient by transforming into a linear programming problem.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…The covariance of the state estimation error for the Mestimators in a dynamic system cannot be obtained easily. Previously in [16], [17] analytical equations for the covariance of the state estimation error of the M -estimator in a static system has been derived. The extension of the analytical covariance equation in [16], [17] to dynamic state estimation is not obvious.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…Previously in [16], [17] analytical equations for the covariance of the state estimation error of the M -estimator in a static system has been derived. The extension of the analytical covariance equation in [16], [17] to dynamic state estimation is not obvious. Unlike a static system, the process noise and the process matrix in a dynamic system are not assumed zero and the identity matrix respectively [4].…”

Section: B Literature Reviewmentioning

confidence: 99%

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Covariance Analysis of LAV Robust Dynamic State Estimation in Power Systems

Sun

Chen

et al. 2020

IEEE Systems Journal

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In power system state estimation, the robust Least Absolute Value robust dynamic estimator is well-known. However, the covariance of the state estimation error cannot be obtained easily. In this paper, an analytical equation is derived using Influence Function approximation to analyze the covariance of the robust Least Absolute Value dynamic state estimator. The equation gives insights into the precision of the estimation and can be used to express the variances of the state estimates as functions of measurement noise variances, enabling the selection of sensors for specified estimator precision. Simulations on the IEEE 14-bus, 30-bus and 118-bus systems are given to illustrate the usefulness of the equation. Monte-Carlo experiments can also be used to determine the covariance, but many data points are needed and hence many runs are required to achieve convergence. Our result shows that to obtain the covariance of the state estimation error, the analytical equation proposed in this paper is four-order of magnitude faster than a 10,000-run Monte-Carlo experiment on both the IEEE 14-bus and 30-bus systems.

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Section: B Literature Reviewmentioning

confidence: 99%

Section: B Literature Reviewmentioning

confidence: 99%

Section: B Literature Reviewmentioning

confidence: 99%

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Covariance Analysis of LAV Robust Dynamic State Estimation in Power Systems

Sun

Chen

et al. 2020

IEEE Systems Journal

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“…However, it is possible to list here some issues that have (and will continue to have) an impact on the SE function, namely: Different ways of processing corrupted measurements are topics of permanent interest in the SE development. Robust algorithms capable of further mitigating the influence of corrupted measurements—eg, promising algorithms based on the moving horizon strategy (sliding window of past measurements)—have been recently proposed. In addition, approaches that lead to the creation of algorithms competent in processing uncorrelated measurement residuals deserve further research. Various techniques, borrowed from the computer science field and applied to SE—based for instance on artificial intelligence, information theory, data integration, and evolutionary computing techniques—can integrate promising research directions. Deregulation of energy markets requires power companies to supervise their networks over vast areas, which entails the development of distributed (multi‐area) SE, aiming at the enhancement of the computational performance and reliability of the estimation process.…”

Section: Future Prospectsmentioning

confidence: 99%

Roots, achievements, and prospects of power system state estimation: A review on handling corrupted measurements

Filho

Souza

Glover³

2018

Int Trans Electr Energ Syst

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Summary State estimation (SE) is an important element of power network analysis applications implemented at control centres, being instrumental in providing reliable data on the system operating conditions. Looking back, power system SE can be considered an extremely active and argumentative research field. Despite remarkable achievements reached to date, how to process corrupted measurements still raises much interest. Hence, the need for this review paper that aims at fostering the discussion of the following aspects when handling corrupted measurements: detection, identification, and elimination/substitution of spurious measurements; observability and criticality conditions; SE and forecasting; residual and innovation tests; conventional and phasor measurements. In the golden jubilee of SE (1968‐2018), naturally influential works on the subject (some of them with historic significance) are highlighted. In doing so, the objective is to make the paper a stimulating reading for both experienced professionals and newcomers to the field. Comments on promising research directions that give a fresh perspective to SE are also provided. Finally, it is hoped that this review paper can contribute towards the improvement of SE applications, in light of enhanced bad data analyses.

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“…The optimum match and the sensitivity levels in the range analysis are obtained by a small amount of calculations. This method ignores the inevitable errors in the test process [39,40]. And the variance analysis and significance check to judge the significance according to the F distribution function, will just make up for this deficiency.…”

Section: Pc Analysismentioning

confidence: 99%

Solidification of High Organic Matter Content Sludge by Cement, Lime and Metakaolin

Ke¹,

Wang

Tan³

et al. 2018

Period. Polytech. Civil Eng.

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Based on orthogonal experimental design, the key solidification controlling technology of Solidified/Stabilized (S/S) sludge with high total organic content (TOC) by cement, lime and metakaolin was explored by macroscopic tests, chemical components measurements and microscopic analysis. The macroscopic tests show that, the permeability coefficient is mainly affected by initial water content and lime content, and the unconfined compression strength is mainly affected by cement content and lime content. The chemical components measurements show that, the solidification effect of S/S sludge with high TOC is controlled by organic matter consumption, and organic matter consumption is determined by the alkaline environment from the cement and lime hydration reactions, which is mainly affect by the initial water content and lime-metakaolin content ratio. The microscopic analysis results show that, lime consumes parts of organic matter while excess lime produces weak Ca(OH)2 crystal fluffy sheet structure, matakaolin produces pozzolanic reactions with cement and lime instead of soil particles, and consumes the weak Ca(OH)2 crystal fluffy sheet structure produced by superfluous lime. The research has confirmed key controlling points of S/S sludge in case of high TOC, which will provide theoretical guidance and technical support for S/S sludge promotion with high TOC.

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Variance analysis of robust state estimation in power system using influence function

Cited by 19 publications

References 24 publications

Covariance Analysis of LAV Robust Dynamic State Estimation in Power Systems

Covariance Analysis of LAV Robust Dynamic State Estimation in Power Systems

Roots, achievements, and prospects of power system state estimation: A review on handling corrupted measurements

Solidification of High Organic Matter Content Sludge by Cement, Lime and Metakaolin

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