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1
Windproof ability of aerodynamic measures to improve the wind environment above a truss girder

Zewen Wang,Haojun Tang,Yongle Li,Junjie Guo,Zhanhui Liu 한국풍공학회 2021 Wind and Structures, An International Journal (WAS Vol.32 No.5
Aerodynamic measures have been widely used for improving the flutter stability of long-span bridges, and this paper focuses their windproof ability to improve the wind environment for vehicles. The whole wind environment around a long-span bridge located in high altitude mountainous areas is first studied. The local wind environment above the deck is then focused by two perspectives. One is the windproof effects of aerodynamic measures, and the other is whether the bridge with aerodynamic measures meets the requirement of flutter stability after installing extra wind barriers in the future. Furthermore, the effects of different wind barriers are analyzed. Results show that aerodynamic measures exert potential effects on the local wind environment, as the vertical stabilizer obviously reduces wind velocities behind it while the closed central slot has limited effects. The suggested aerodynamic measures have the ability to offset the adverse effect of the wind barrier on the flutter stability of the bridge. Behind the wind barrier, wind velocities decrease in general, but in some places incoming flow has to pass through the deck with higher velocities due to the increase in blockage ratio. Further comparison shows that the wind barrier with four bars is optimal.
2
Detection of Voltage Sag using An Adaptive Extended Kalman Filter Based on Maximum Likelihood

Xi, Yanhui,Li, Zewen,Zeng, Xiangjun,Tang, Xin The Korean Institute of Electrical Engineers 2017 Journal of Electrical Engineering & Technology Vol.12 No.3
- 원문보기
An adaptive extended Kalman filter based on the maximum likelihood (EKF-ML) is proposed for detecting voltage sag in this paper. Considering that the choice of the process and measurement error covariance matrices affects seriously the performance of the extended Kalman filter (EKF), the EKF-ML method uses the maximum likelihood method to adaptively optimize the error covariance matrices and the initial conditions. This can ensure that the EKF has better accuracy and faster convergence for estimating the voltage amplitude (states). Moreover, without more complexity, the EKF-ML algorithm is almost as simple as the conventional EKF, but it has better anti-disturbance performance and more accuracy in detection of the voltage sag. More importantly, the EKF-ML algorithm is capable of accurately estimating the noise parameters and is robust against various noise levels. Simulation results show that the proposed method performs with a fast dynamic and tracking response, when voltage signals contain harmonics or a pulse and are jointly embedded in an unknown measurement noise.
3
Detection of Voltage Sag using An Adaptive Extended Kalman Filter Based on Maximum Likelihood

Yanhui Xi,Zewen Li,Xiangjun Zeng,Xin Tang 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.3
- 원문보기
An adaptive extended Kalman filter based on the maximum likelihood (EKF-ML) is proposed for detecting voltage sag in this paper. Considering that the choice of the process and measurement error covariance matrices affects seriously the performance of the extended Kalman filter (EKF), the EKF-ML method uses the maximum likelihood method to adaptively optimize the error covariance matrices and the initial conditions. This can ensure that the EKF has better accuracy and faster convergence for estimating the voltage amplitude (states). Moreover, without more complexity, the EKF-ML algorithm is almost as simple as the conventional EKF, but it has better anti-disturbance performance and more accuracy in detection of the voltage sag. More importantly, the EKF-ML algorithm is capable of accurately estimating the noise parameters and is robust against various noise levels. Simulation results show that the proposed method performs with a fast dynamic and tracking response, when voltage signals contain harmonics or a pulse and are jointly embedded in an unknown measurement noise.