Research On Heliostat And Cloud Detection In Heat Collection Island Of Tower Power Station

Concentrating Solar Power(CSP)technology is one of the important ways to solve the problems of energy shortage and improvement of the environment in the 21 st century,and solar tower power technology has become an important development trend in the field of CSP with its advantages such as clean and sustainable power generation.The heat concentrating island is the main component of the solar tower power system,which can collect solar radiation energy.The safety and stability of its operation are related to the power generation safety and efficiency of the whole power station,and it is an important research direction in the solar tower power system.This thesis mainly focuses on the heliostats field in the heat concentrating island of the solar tower power station,including malfunction heliostat detection,heliostat error measurement,cloud monitoring over the heliostats field,and so on.This work contents presented as follows:(1)The mathematical model of solar motion is built,and the tracking trajectory model of the heliostat is also established based on the principle of optical reflection.The vernal equinox,summer solstice,autumn equinox and winter solstice are selected for simulation to verify the correctness of the model.At the same time,the arrangement mode of heliostats field and unmanned aerial vehicle(UAV)aerial photography planning are studied to lay a foundation for malfunction heliostat detection.(2)A method to detect malfunction heliostat based on aerial images is proposed,which uses UAV to photograph the whole heliostats field,then image processing techniques such as median filter and canny edge detection are used to handle the taken image.Malfunction heliostat is detected by edge features,and a cascade extraction method is designed to solve some problems such as shadow obscured in images.(3)A method to measure heliostat error based on a laser spot is proposed.The error types of heliostat are analyzed,and then the two mathematical models are established for calculating errors and simulating analysis.The method of replacing the sunlight with laser is put forwarded,and the center of the spot can be obtained by fitting the spot with the image processing technology in the night environment,finally,the error parameters are obtained according to the error calculation model.(4)A method to measure the moving velocity of cloud based on image segmentation is proposed.Taking pictures of the clouds above the heliostats field by a fish-eye camera,and then segment the taken cloud images by a genetic algorithm.A method of object detection and matching based on the connected domain is proposed to calculate and forecast the speed and direction of the moving clouds.The position relation of sun,cloud,and heliostat field is established,and the histogram comparison method is used to judge whether the sun is covered by moving clouds.The experimental results show that proposed the malfunction heliostat detection method based on aerial photography has high detection accuracy,and UAV inspection has the advantages of low cost,extensiveness,etc.The error measurement method based on laser spot has high accuracy.The proposed cloud velocity measurement method based on image segmentation has a good performance for cloud movement forecasting,and the histogram comparison method also has a good judgment performance for cloud cover.The above research content provides a theoretical basis for the development of heliostat and cloud detection in the heat concentrating island of the solar tower power station.