Filtering of the ARMAX Process with Generalized <i>t</i>-Distribution Noise: The Influence Function Approach

Ho, Weng Khuen; Ling, Keck Voon; Vu, Hoang Dung; Wang, Xiaoqiong

doi:10.1021/ie401990x

Cited by 19 publications

(12 citation statements)

References 22 publications

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“…However, this assumption is only an approximation to reality. For example, transient data in steady-state measurement, instrument failure, human error or model non-linearity can generate non-Gaussian measurement errors [7], [8]. Outliers that are far away from the expected Gaussian distribution function can give rise to misleading estimation results.…”

Section: A Motivationmentioning

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: A Motivationmentioning

confidence: 99%

“…( 28) is not affected by G and u(k). The effect of G and u(k) is canceled out in (7) and (21). This is because the control term Gu(k) is assumed to be known exactly and does not affect the precision of the state estimation [32].…”

Section: Examplesmentioning

confidence: 99%

Covariance Analysis of LAV Robust Dynamic State Estimation in Power Systems

Sun

Chen

et al. 2020

IEEE Systems Journal

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“…In practice, the noise is usually not a Gaussian distribution, and the ARMAX filter could be applied to better handle the noise. 36 In this article, for simplicity, the noise is assumed to be Gaussian distributed, and the classical Kalman filter is employed as the observer.…”

Section: Control Synthesis and Stability Analysismentioning

confidence: 99%

Transient uniformity model predictive control in dealing with non-uniformity of multivariable systems

Wang

Incecik

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part M:

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In this article, the model predictive control scheme is studied for a class of multiple-input multiple-output linear systems with different transient trajectories among the outputs. To eliminate the transient errors among the outputs, a modified model predictive control scheme is designed making one output track following the reference while the other outputs track following this output instead of the reference. Utilizing the specified output as the alternative reference for all the other outputs, a constructive model predictive control scheme is developed to diminish the differences of the transient-state trajectories among each output such that both the uniformity of the transient-state trajectories and the optimal steady-state trajectories are achieved. The effectiveness of the proposed transient uniformity model predictive control scheme is demonstrated in the experiment of the semiconductor wafer manufacturing baking process. The experimental results show that the transient uniformity model predictive control scheme has successfully reduced the integral square error of transient-state uniformity between any two outputs by 90% as compared to the conventional model predictive control scheme. The robustness of the proposed scheme has also been experimentally evaluated in the presence of external disturbance and plant modeling inaccuracy.

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“…However, this assumption is only an approximation to reality. For example, transient data in steady-state measurement, instrument failure, human error or model nonlinearity can generate non-Gaussian measurement errors [13,14]. Outliers that are far away from the expected Gaussian distribution function can give rise to erroneous estimation results [15].…”

Section: Motivationmentioning

confidence: 99%

“…Using influence function (IF) approximation, an analytical equation is derived to approximately calculate the variances of robust estimators such as QC, QL, SR, SHGM and MS. The IF has previously been used as an analytical tool in robust statistics [25,26] and filter design with non-Gaussian noise assumption [13]. The derived analytical variance equation is useful.…”

Section: Major Contributions Of the Thesismentioning

confidence: 99%

Robust and distributed state estimation for power systems

Chen¹

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for his unconditional support, professional guidance, understanding and inspiration during my PhD study. Not only did his insights help to shape this work, but his aesthetic sense taught me what good research is truly about. The attitudes towards work I learned from him are logical, tolerant and careful, which will definitely be my valuable assets. I really appreciate his best effort to provide me a good platform for doing research. He is more than a thesis supervisor to me.It has been also a great experience to work with Associate Professor Ho Weng Khuen from the National University of Singapore. He is more like my thesis cosupervisor. I am always impressed by his expertise on pointing out the issues and on the way to solve research problems. In addition, I would like to thank Prof. Jan M. Maciejowski from University of Cambridge for his valuable suggestions in my research direction.All my friends at Nanyang Technological University made my study and life to be wonderful and memorable. In particular, I am very thankful to Zhou Dexiang, Sun Lu and Chen Xuebing, Dr. Eddy Foo, Ashok, who helped me to understand so many critical problems. I am thankful to Dr. Tri Tran for the discussions on some conference papers and report. I would like to thank my friends, Yang Cangjie,

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Filtering of the ARMAX Process with Generalized t-Distribution Noise: The Influence Function Approach

Cited by 19 publications

References 22 publications

Covariance Analysis of LAV Robust Dynamic State Estimation in Power Systems

Covariance Analysis of LAV Robust Dynamic State Estimation in Power Systems

Transient uniformity model predictive control in dealing with non-uniformity of multivariable systems

Robust and distributed state estimation for power systems

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