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Full-Text Articles in Engineering
Smart Power Grid Synchronization With Fault Tolerant Nonlinear Estimation, Xin Wang, Edwin E. Yaz
Smart Power Grid Synchronization With Fault Tolerant Nonlinear Estimation, Xin Wang, Edwin E. Yaz
Electrical and Computer Engineering Faculty Research and Publications
Effective real-time state estimation is essential for smart grid synchronization, as electricity demand continues to grow, and renewable energy resources increase their penetration into the grid. In order to provide a more reliable state estimation technique to address the problem of bad data in the PMU-based power synchronization, this paper presents a novel nonlinear estimation framework to dynamically track frequency, voltage magnitudes and phase angles. Instead of directly analyzing in abc coordinate frame, symmetrical component transformation is employed to separate the positive, negative, and zero sequence networks. Then, Clarke's transformation is used to transform the sequence networks into the αβ …
Stochastic Stability Of The Continuous-Time Extended Kalman Filter, K. Reif, S. Gunther, Edwin E. Yaz, R. Unbehauen
Stochastic Stability Of The Continuous-Time Extended Kalman Filter, K. Reif, S. Gunther, Edwin E. Yaz, R. Unbehauen
Electrical and Computer Engineering Faculty Research and Publications
he error behaviour of the extended Kalman filter is analysed. It is proved that the estimation error remains bounded if the system satisfies a detectability condition and both the initial estimation error and the disturbing noise terms are small enough. Moreover, some selected cases with both bounded and unbounded estimation error are demonstrated by numerical simulations.
Sliding Mode Measurement Feedback Control For Antilock Braking Systems, Cem Unsal, Pushkin Kachroo
Sliding Mode Measurement Feedback Control For Antilock Braking Systems, Cem Unsal, Pushkin Kachroo
Electrical & Computer Engineering Faculty Research
We describe a nonlinear observer-based design for control of vehicle traction that is important in providing safety and obtaining desired longitudinal vehicle motion. First, a robust sliding mode controller is designed to maintain the wheel slip at any given value. Simulations show that longitudinal traction controller is capable of controlling the vehicle with parameter deviations and disturbances. The direct state feedback is then replaced with nonlinear observers to estimate the vehicle velocity from the output of the system (i.e., wheel velocity). The nonlinear model of the system is shown locally observable. The effects and drawbacks of the extended Kalman filters …
Hybrid Kalman / Minimax Filtering In Phase-Locked Loops, Daniel J. Simon, Hossny El-Sherief
Hybrid Kalman / Minimax Filtering In Phase-Locked Loops, Daniel J. Simon, Hossny El-Sherief
Electrical and Computer Engineering Faculty Publications
A method of combining Kahnan filtering and minimax filtering is proposed and demonstrated in an application to phase-locked loop design. Kalman filtering suffers from a lack of robustness to departures from the assumed noise statistics. Minimax filtering, however, has the drawback of ignoring the engineer's (admittedly incomplete) knowledge of the noise statistics. It is shown in this paper that hybrid Kalman/minimax filtering can provide the “best of both worlds” . Phase-locked loop filter design is used in this paper to demonstrate an application of hybrid estimation.
Classification Using Set-Valued Kalman Filtering And Levi's Decision Theory, T.K. Moon, Scott E. Budge
Classification Using Set-Valued Kalman Filtering And Levi's Decision Theory, T.K. Moon, Scott E. Budge
Electrical and Computer Engineering Faculty Publications
We consider the problem of using Levi's expected epistemic decision theory for classification when the hypotheses are of different informational values, conditioned on convex sets obtained from a set-valued Kalman filter. The background of epistemic utility decision theory with convex probabilities is outlined and a brief introduction to set-valued estimation is given. The decision theory is applied to a classifier in a multiple-target tracking scenario. A new probability density, appropriate for classification using the ratio of intensities, is introduced.
Epistemic Decision Theory Applied To Multiple-Target Tracking, Wynn C. Stirling, T. K. Moon, S. E. Budge, J. B. Thompson
Epistemic Decision Theory Applied To Multiple-Target Tracking, Wynn C. Stirling, T. K. Moon, S. E. Budge, J. B. Thompson
Faculty Publications
A decision philosophy that seeks the avoidance of error by trading off belief of truth and value of information is applied to the problem of recognizing tracks from multiple targets (MTT). A successful MTT methodology should be robust in that its performance degrades gracefully as the conditions of the collection become less favorable to optimal operation. By stressing the avoidance, rather than the explicit minimization, of error, the authors obtain a decision rule for trajectory-data association that does not require the resolution of all conflicting hypotheses when the database does not contain sufficient information to do so reliably. This rule, …