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[IEEE 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA) - Paris, France (2018.10.14-2018.10.17)] 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA) - Improved Matlab Simulink Two-diode Model of PV Module and Method of Fast Large-Scale PV System Simulation
摘要： Nowadays, to response to the demand of a high rate renewable energy penetration in the electricity grids, the installation of many new large-scale photovoltaic systems up to more than 100 MW is unavoidable. However, in many countries, there is a serious lack of space for these large-scale projects. As the authorities wish to avoid taking away many farmlands for ground-mound photovoltaic systems, we must find acceptable ecological alternative solutions. One of the solution is the linear photovoltaic systems due to the availability and ready to use of the linear space: bike path, along the pavement of a road, along the edge of a highway… Another interesting method is the installation of floating PV power plants on inland water bodies. The possibilities and opportunities of these new kinds of large-scale PV plants are infinite. However, their installations meet a lot of challenges, such as the loss due to new structures and environments, the cost of equipment, the compatibility of the new PV plants with the initial function of the surface… To study these challenges, an effective simulation tool is essential. Considering the importance of the simulation tools, this paper proposes an improved PV system simulation model based on the two-diode model and a fast, comprehensive method to simulate the PV system in long term with a huge volume of the MATLAB/SIMULINK in environment.
关键词： PV module database,Large-scale photovoltaic system,Module modelisation,Photovoltaic (PV),Matlab/Simulink
更新于2025-09-04 15:30:14
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Real-time car tracking system based on surveillance videos
摘要： As a variety of video surveillance devices such as CCTV, drones, and car dashboard cameras have become popular, numerous studies have been conducted regarding the effective enforcement of security and surveillance based on video analysis. In particular, in car-related surveillance, car tracking is the most challenging task. One early approach to accomplish such a task was to analyze frames from different video sources separately. Considering the shooting range of the bulk of video devices, the outcome from the analysis of single video source is highly limited. To obtain more comprehensive information for car tacking, a set of video sources should be considered together and the relevant information should be integrated according to spatial and temporal constraints. Therefore, in this study, we propose a real-time car tracking system based on surveillance videos from diverse devices including CCTV, dashboard cameras, and drones. For scalability and fault tolerance, our system is built on a distributed processing framework and comprises a Frame Distributor, a Feature Extractor, and an Information Manager. The Frame Distributor is responsible for distributing the video frames from various devices to the processing nodes. The Feature Extractor extracts principal vehicle features such as plate number, location, and time from each frame. The Information Manager stores all the features into a database and handles user requests by collecting relevant information from the feature database. To illustrate the effectiveness of our proposed system, we implemented a prototype system and performed a number of experiments. We report some of the results.
关键词： Computer vision,Automobile tracking system,Real-time,Index structure,Database
更新于2025-09-04 15:30:14