极地研究

极地研究 ›› 2022, Vol. 34 ›› Issue (2): 198-209.DOI: 10.13679/j.jdyj.20210027

• 研究论文 • 上一篇    下一篇

南极内陆科研观测舱体内部保障与控制系统的应用与研究

李聪克1,2,李丙瑞2,王焘2,陈燕1,窦银科1,姚旭2,王煜尘1   

  1. 1太原理工大学电气与动力工程学院, 山西 太原 030024;
    2    中国极地研究中心, 上海 200136
  • 收稿日期:2021-03-15 修回日期:2021-04-15 出版日期:2022-06-30 发布日期:2022-06-15
  • 通讯作者: 王焘
  • 基金资助:
    国家自然科学基金项目(41776199)

Application and research for the internal support and control system of the Antarctic inland scientific research observation cabin

Li Congke1,2, Li Bingrui2, Wang Tao2, Chen Yan1, Dou Yinke1, Yao Xu2, Wang Yuchen1   

  1. 1 College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China;
    2 Polar Research Institute of China, Shanghai 200136, China
  • Received:2021-03-15 Revised:2021-04-15 Online:2022-06-30 Published:2022-06-15

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摘要： 为了更好地解决南极高空自动化监测系统在南极内陆地区不必要的能量损耗和因低温导致的设备无法正常工作的问题, 提出了一种可对未来一天环境温度进行预测的方法, 以及可根据不同的温差选择相应加热功率的策略。该自动化监测系统由地表新能源发电部分、地表观测舱和地下能源舱三部分组成。通过分析南极伊丽莎白公主地地表的气象数据, 得出温度是影响当地设备正常运行的主要因素。通过建立热网络方程, 得到加热功率与温差的关系, 并对比4种神经网络预测算法, 选择出最合适的深度学习算法用于对未来环境温度进行预测, 从而确立了一种可保证地表观测舱以低能耗持续观测的控制策略。该系统在我国第36次南极科学考察中部署在伊丽莎白公主地, 系统已稳定运行10个月, 证明了该控制策略的实用性以及设备的可靠性。

关键词: 南极, 神经网络, 控制策略, 应用

Abstract:

To solve the problems of unnecessary energy loss and equipment failure of the Antarctic high-altitude automatic monitoring system caused by low temperatures in the Antarctic inland area, we propose a method to predict the ambient temperature on the next day and a strategy to select the corresponding heating power according to different temperature difference. The automatic monitoring system is composed of three parts: the surface new energy generation system, the surface observation cabin and the underground energy cabin. Through an analysis of the meteorological data on the surface of Princess Elizabeth Land, we find that temperature is the main factor affecting the normal operation of local equipment. By establishing thermal network equation, we obtain the relationship between heating power and temperature. Moreover, after comparing four kinds of neural network prediction algorithm, we select the most appropriate deep learning algorithm to forecast the future environmental temperature. Thus, we propose a control strategy that guarantees continuous observation of the surface observation cabin with low energy costs. The system was deployed in Princess Elizabeth Field during China’s 36th Antarctic Scientific Expedition. The system has been running stably for 10 months, which proves the practicability of the control strategy and the reliability of the equipment.

Key words: Antarctica, neural network, control strategy, application