武大AiFlow视觉测流技术在天生桥一级水电站上游水情监测中的应用研究
Research and Application of Wuhan University AiFlow Visual Flowmeter Technology for Upstream Hydrological Monitoring at the Tianshengqiao First Level Hydropower Plant
DOI: 10.12677/jwrr.2024.133032, PDF,    科研立项经费支持
作者: 刘建华, 胡召根:天生桥一级水电开发有限责任公司水力发电厂,贵州 兴义;乐 红, 陈 杰, 陈 华:武汉大学水资源工程与调度全国重点实验室,湖北 武汉;孙 豹, 高亚芬, 邱 珊:武汉大水云科技有限公司,湖北 武汉
关键词: 武大AiFlow视觉测流技术上游水情监测天生桥一级水电站Wuhan University AiFlow Visual Flowmeter Technology Upstream Hydrological Monitoring Tianshengqiao First Level Hydropower Plant
摘要: 本文针对天生桥一级水电站上游的水情监测,基于视觉测流原理,设计并开发了一套低成本、高效的图像采集与处理系统,并与走航式ADCP进行了比测分析。结果表明,视觉测流虚流量与比测流量相关关系拟合曲线的可决系数R2达到了0.99,系统误差为−0.44%,随机不确定度为9.87%,符合一类精度站的流量测验允许误差标准,验证了视觉测流系统的准确性和稳定性,为天生桥一级水电站的水库调度提供重要数据支持。
Abstract: This paper focuses on hydrological monitoring upstream of the Tianshengqiao First Level Hydropower Plant. Based on the principle of visual flow measurement, a low-cost and efficient image acquisition and processing system was designed and developed. A comparative analysis was conducted with the mobile ADCP. The results show that the coefficient of determination (R2) of the fitting curve between the visual flow measurement and the comparative flow measurement reached 0.99. The system error was −0.44%, and the random uncertainty was 9.87%, which meets the allowable error standard for flow measurement of Class I accuracy stations. This validates the accuracy and stability of the visual flow measurement system, providing important data support for the reservoir dispatching of the Tianshengqiao First Level Hydropower Plant.
文章引用:刘建华, 乐红, 胡召根, 陈杰, 孙豹, 高亚芬, 邱珊, 陈华. 武大AiFlow视觉测流技术在天生桥一级水电站上游水情监测中的应用研究[J]. 水资源研究, 2024, 13(3): 273-282. https://doi.org/10.12677/jwrr.2024.133032

参考文献

[1] GB 50179-2015. 河流流量测验规范[S]. 北京: 中国计划出版社, 2016.
[2] 夏帆, 陈莹, 窦明, 等. 水资源空间均衡系数计算方法及其应用[J]. 水资源保护, 2020, 36(1): 52-57.
[3] ZHANG, Z. L., SUN, H. and SU, Y. Water use efficiency and its influencing factors in arid areas of northwest China. Journal of Ecology and Rural Environment, 2017, 33(11): 961-967.
[4] YANG, Y. Q., ZHANG, J. Y., YAN, W. M., et al. Impact assessment of water diversion project on urban aquatic ecological environment. Ecological Indicators, 2021, 125: 107496.[CrossRef
[5] FUJITA, I., WATANABE, H. and TSUBAKI, R. Development of a non-intrusive and efficient flow monitoring technique: The space-time image velocimetry (STIV). International Journal of River Basin Management, 2007, 5(2): 105-114.[CrossRef
[6] FUJITA, I., KITADA, M., SHIMONO, M., et al. Spatial measurements of snowmelt flood by image analysis with multiple-angle images and radio-controlled ADCP. Journal of JSCE, 2017, 5(1): 305-312. [Google Scholar] [CrossRef
[7] 王慧斌, 董伟, 张振, 等. 基于时空图像频谱的时均流场重建方法[J]. 仪器仪表学报, 2015, 36(3): 623-631.
[8] 赵浩源, 陈华, 刘维高, 黄凯霖, 刘炳义, 刘德地, 王俊. 基于河流表面时空图像识别的测流方法[J]. 水资源研究, 2020, 9(1): 1-11. [Google Scholar] [CrossRef
[9] ZHAO, H., CHEN, H., LIU, B., et al. An improvement of the space-time image velocimetry combined with a new denoising method for estimating river discharge. Flow Measurement and Instrumentation, 2021, 77: 101864.[CrossRef