研究目的
To develop a camera model and automated calibration procedure for stationary daytime sky imaging cameras used in solar power forecasting, utilizing sun position for calibration without special equipment.
研究成果
The camera model and calibration procedure are effective for fisheye lenses, with calibration errors around 1 pixel on clear days. The method is robust to measurement noise and can be automated, making it suitable for solar forecasting applications. Future work could improve sun detection under cloudy conditions and extend to other camera types.
研究不足
The calibration relies on clear sky conditions for accurate sun detection; performance degrades with cloud cover. The method assumes a central camera model and may not account for all lens distortions perfectly. Requires accurate time and location data for solar position modeling.
1:Experimental Design and Method Selection:
The study designs a camera model for fisheye lenses and an automated calibration method using solar observations. It employs a solar position algorithm and image processing for sun detection, with nonlinear optimization (Levenberg-Marquardt algorithm) for parameter estimation.
2:Sample Selection and Data Sources:
Data from a sky imager (USI) deployed at the Southern Great Plains Climate Research Facility, collecting over 1400 images per day on clear and cloudy days. Solar position is modeled using the NREL solar position algorithm.
3:List of Experimental Equipment and Materials:
Allied Vision GE-2040C camera with Sigma circular fisheye lens (
4:5 mm focal length, equisolid angle projection), CCD sensor, and mounting stand. Experimental Procedures and Operational Workflow:
Images are captured every 30 seconds; sun position is detected automatically; calibration involves initialization, linear estimation, and nonlinear optimization in three stages.
5:Data Analysis Methods:
Root mean square error (RMSE), mean absolute error (MAE), and standard deviation (SD) are computed to evaluate calibration performance. Monte Carlo simulations assess parameter uncertainty.
独家科研数据包,助您复现前沿成果,加速创新突破
获取完整内容