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RISS 인기검색어
- 검색키워드
- 저자 : Wencong Su (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources
Wencong Su,Jianhui Wang,Jaehyung Roh IEEE 2014 IEEE transactions on smart grid Vol.5 No.4
<P>Renewable energy resources such as wind and solar are an important component of a microgrid. However, the inherent intermittency and variability of such resources complicates microgrid operations. Meanwhile, more controllable loads (e.g., plug-in electric vehicles), distributed generators (e.g., micro gas turbines and diesel generators), and distributed energy storage devices (e.g., battery banks) are being integrated into the microgrid operation. To address the operational challenges associated with these technologies and energy resources, this paper formulates a stochastic problem for microgrid energy scheduling. The proposed problem formulation minimizes the expected operational cost of the microgrid and power losses while accommodating the intermittent nature of renewable energy resources. Case studies are performed on a modified IEEE 37-bus test feeder. The simulation results demonstrate the effectiveness and accuracy of the proposed stochastic microgrid energy scheduling model.</P>
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Lim, Hansang,Su, Wencong IEEE 2018 IEEE Transactions on Vehicular Technology VT Vol.67 No.10
<P>This paper proposes a distance-based two-stage energy management strategy for power-split plug-in hybrid electric vehicles (PHEVs). One stage is for long-term optimization and the other is for short-term adaptation to actual traffic conditions. Energy consumption in PHEVs depends on the characteristics of the drivetrain as well as the operating conditions such as power demands and their split. Thus, prior to departure, the operating conditions for a whole trip are optimized for the drivetrain characteristics and trip information, which generates optimal speed and state-of-charge profiles. While driving, the operating conditions are adapted to current traffic conditions for a short horizon on the basis of long-term optimization results. In consideration of the changeability of traffic conditions, the proposed energy management strategy is performed in a distance domain, which localizes the effects of changes in traffic conditions on the long-term optimization results. Therefore, this distance-based two-stage strategy improves the balance between the optimality and the real-time computing time, which is suitable for online management. A model for the propulsion system in a PHEV and the energy management strategy were formulated in a distance domain. An estimation of distribution algorithm was used for long-term optimization and local adaptation.</P>
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A Modified Robust Adaptive Super-twisting Sliding Mode Controller for Grid-connected Converters
Guilherme Vieira Hollweg,Wencong Su,Paulo Jefferson Dias de Oliveira Evald,Rodrigo Varella Tambara,Hilton Abílio Gründling 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.10
This work introduces the application of a new adaptive control structure, which is a modification of the robust model reference adaptive controller (RMRAC) and adaptive super-twisting sliding mode (ASTSM). This controller was previously proposed in the literature and applied to the current control of a grid-connected converter under uncertain grid environments. However, its STSM structure used the tracking error signal function as a sliding surface, which tends to impose considerable chattering in the system. The proposed controller maintains the characteristics of the known structure but replaces the signal function in the STSM equations with a sigmoid function, reducing the current tracking error and improving the system regulation since it is smoother. As the control structure lies in RMRAC theory and some core equations change, stability analysis of the adaptation algorithm is also carried out. Experimental results in a 7.5 kW converter are presented in which the known RMRAC-ASTSM controller presents 2.45% total harmonic distortion while the modified adaptive structure obtains 2.22% total harmonic distortion and better regulation performance.
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Lim, Hansang,Mi, Chunting Chris,Su, Wencong IEEE 2017 IEEE Transactions on Vehicular Technology VT Vol.66 No.8
<P>This paper proposes a distance-based two-stage ecological (eco-) driving scheme by using estimation of distribution algorithms (EDA) and model-based prediction of traffic conditions. Before departure, the optimal speed profile for an entire route is generated by an EDA in combination with speedup approaches for a faster computing time, which can optimize the complex cost function of ecodriving without simplification within a reasonably short computing time. This optimization is performed in a distance domain for localizing changes in the optimal speed profile due to traffic conditions while driving. After departure, by taking the optimal speed profile and actual traffic conditions into consideration, the speed profile for a short term-to only the next location-is adapted. In order to reliably react to actual traffic conditions, additional points are interpolated into the long-term distance step and fine control of speeds at the additional points is established, which is based on a predictive model for estimating the spacing to the preceding vehicle. The proposed ecodriving system is evaluated in two types of route conditions, and its results are compared with the optimization result by the quadratic programming method. This comparison shows that an EDA can generate a speed profile with better optimization results in terms of fuel efficiency and driving time within a shorter computing time.</P>
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