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REMOTE SENSING FOR LAND & RESOURCES    1990, Vol. 2 Issue (4) : 53-58     DOI: 10.6046/gtzyyg.1990.04.07
Technology and Methodology |
APPLICATION OF AIRBORNE THERMAL INFRARED REMOTE SENSING TO THE INVESTIGATION OF SURFACE TEMPERATURE FIELD OF COOLING POOL IN THE POWER PLANT
Xiao Jichun1, Wang Zhimin1, Diao Li2
1. Geological Remote Sensing Centre, Ministry of Geology and Mineral Resources;
2. Xinjiang Electric Power Designing Institute
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Abstract  This paper dealt mainly with the method to map the temperature on the surface of cooling pool with the power plant in operation, and to calculate the area of water in the cooling pool and the thermal diffusion area under the condition of lukewarm water discharge in the powel plant, according to the aitborne thermal infrared scanning images of the cooling pool in Hongyanchi Power Plant in Xinjiang Autonomous Region obtained under surface wind with lm/s and 7m/s speed, and processed on S101 computer system. A preliminary analysis was made on thermal diffusion law and water surface cooling capacity of the cooling pool. Practice showed that the. investigated data have important consulting significance for the operation management of this power plant, rebuilding the layout of water intake and water outlet, and the design of expanding installed capacity.
Keywords CBERS      Wetlands      Land cover      Classification      Object-oriented     
Issue Date: 02 August 2011
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YU Huan,ZHANG Shu-Qing,CUI Li, et al. APPLICATION OF AIRBORNE THERMAL INFRARED REMOTE SENSING TO THE INVESTIGATION OF SURFACE TEMPERATURE FIELD OF COOLING POOL IN THE POWER PLANT[J]. REMOTE SENSING FOR LAND & RESOURCES, 1990, 2(4): 53-58.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.1990.04.07     OR     https://www.gtzyyg.com/EN/Y1990/V2/I4/53


[1] 陈惠泉、吴江航:我国电厂冷却水研究的回顾和展望,水利水电科技进展,第二册,电力工业出版社,1981年。
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