Kalman Filter-Based Closed Cycle Steam Temperature Processing Method

GAO Huizhong; LIU Yang; MA Weifeng; ZONG Xiao; GUO Zhaoyuan

doi:10.11993/j.issn.2096-3920.2022-0089

Volume 31 Issue 5

Oct 2023

Turn off MathJax

Article Contents

Article Navigation > Journal of Unmanned Undersea Systems > 2023 > 31(5): 771-777

GAO Huizhong, LIU Yang, MA Weifeng, ZONG Xiao, GUO Zhaoyuan. Kalman Filter-Based Closed Cycle Steam Temperature Processing Method[J]. Journal of Unmanned Undersea Systems, 2023, 31(5): 771-777. doi: 10.11993/j.issn.2096-3920.2022-0089

Citation:

GAO Huizhong, LIU Yang, MA Weifeng, ZONG Xiao, GUO Zhaoyuan. Kalman Filter-Based Closed Cycle Steam Temperature Processing Method[J]. Journal of Unmanned Undersea Systems, 2023, 31(5): 771-777. doi: 10.11993/j.issn.2096-3920.2022-0089

Citation:

PDF( 1748 KB)

Kalman Filter-Based Closed Cycle Steam Temperature Processing Method

doi: 10.11993/j.issn.2096-3920.2022-0089

The 705 Research Institute, China State Shipbuilding Corporation Limited, Xi’an 710077, China

Received Date: 2022-12-07
Accepted Date: 2023-02-21
Rev Recd Date: 2023-01-10

Available Online: 2023-10-12

Abstract

Abstract

Li/SF₆ energy system is a new type of high energy power source, which can support the construction of a closed cycle power system for underwater equipment due to its high energy density and no product emission. As a key feedback parameter that affects the reaction process, the helical tube outlet temperature in the energy system will impact the precision and stability of the system control under the disturbance of measurement noise and system noise, bringing a negative effect on the reliability of the vehicle. In this paper, the steam outlet temperature curve was obtained by one-dimensional distributed parameter simulation. In view of the shortcomings of common noise reduction methods, an online noise reduction method based on Kalman filter principle was proposed. By comparing with the conventional sliding average filtering and first-order low-pass filtering methods, it can be seen that the proposed method has distinct advantages in terms of error probability distribution and signal-to-noise ratio. Besides, it can shorten the stable time and improve the dynamic characteristics of the system.
- closed cycle,
- Kalman filter,
- steam temperature,
- signal processing

FullText(HTML)

References(18)

References

[1]	黄庆, 卜建杰, 郑邯勇. Li/SF₆热源在鱼雷和UUV推进系统中的应用[J]. 舰船科学技术, 2006, 28(2): 67-71. Huang Qing, Bu Jianjie, Zheng Hanyong. The application of Li/SF₆ heat source in the torpedo and the UUV propulsion systems[J]. Ship Science and Technology, 2006, 28(2): 67-71.
[2]	白杰, 党建军, 曹蕾蕾. 基于Li/SF₆能源的新型UUV动力系统热力性能分析[J]. 水下无人系统学报, 2019, 27(2): 212-216. Bai Jie, Dang Jianjun, Cao Leilei. Thermodynamic performance analysis of a new type of UUV power system based on Li/SF₆ energy[J]. Journal of Unmanned Undersea Systems, 2019, 27(2): 212-216.
[3]	白杰. 无人水下航行器新型热电联合闭式循环动力系统研究[D]. 西安: 西北工业大学, 2016.
[4]	陈支厦, 郑邯勇, 赵文忠, 等. 鱼雷热动力能源研究现状及发展趋势[C]//OSEC首届兵器工程大会论文集. 重庆: OSEC, 2017.
[5]	朱强, 郑邯勇, 王树峰, 等. 金属燃料动力系统在鱼雷和UUV上的应用[C]//OSEC首届兵器工程大会论文集. 重庆: OSEC, 2017.
[6]	Wang G, Yang Y, Wang S. Ocean thermal energy application technologies for unmanned underwater vehicles: A comprehensive review[J]. Applied Energy, 2020, 278: 115752. doi: 10.1016/j.apenergy.2020.115752
[7]	Waters D F, Cadou C P. Modeling a hybrid rankine-cycle/fuel-cell underwater propulsion system based on aluminum-water combustion[J]. Journal of Power Sources, 2013, 221(1): 272-283.
[8]	刘景云. Li/SF₆双闭环动力系统自适应控制技术研究[C]//中国造船工程学会船舶力学学术委员会测试技术学组2021年学术会议论文集. 昆明: 中国造船工程学会, 2021.
[9]	张艳杰, 梁鉴如, 马强, 等. 光纤温度传感系统中信号去噪方法[J]. 传感器与微系统, 2017, 36(12): 19-21. Zhang Yanjie, Liang Jianru, Ma Qiang, et al. Signal denoising method in optical fiber temperature sensing system[J]. Transducer and Microsystem Technologies, 2017, 36(12): 19-21.
[10]	于洋, 李杰, 余松, 等. 基于卡尔曼滤波的电磁流量计信号处理[J]. 电子测量与仪器学报, 2022, 36(9): 183-189. Yu Yang, Li Jie, Yu Song, et al. Kalman filter-based electromagnetic flowmeter signal processing[J]. Journal of Electronic Measurement and Instrumentation, 2022, 36(9): 183-189.
[11]	卢胜利, 刘美玲, 田彦彦. 基于卡尔曼滤波的多温度传感器数据融合系统[J]. 现代科学仪器, 2013(1): 65-68. Lu Shengli, Liu Meiling, Tian Yanyan. Data fusion system of multi temperature sensor based on Kalman filter[J]. Modern Scientific Instruments, 2013(1): 65-68.
[12]	张春路. 制冷空调系统仿真原理与技术[M]. 北京: 化学工业出版社, 2012: 77-78.
[13]	杨世铭, 陶文铨. 传热学[M]. 北京: 高等教育出版社, 2006.
[14]	刘芬, 范洪强, 吕涛, 等. 基于卡尔曼滤波的含噪声小样本数据处理方法[J]. 上海大学学报(自然科学版), 2022, 28(3): 427-439. Liu Fen, Fan Hongqiang, Lü Tao, et al. Kalman filter based method for processing small noisy sample data[J]. Journal of Shanghai University (Natural science edition), 2022, 28(3): 427-439.
[15]	梁民赞, 陆扬, 周新鹏. 一种抑制卡尔曼滤波发散的实时数据处理方法[J]. 声学技术, 2008(5): 761-764. Liang Minzan, Lu Yang, Zhou Xinpeng. A real-time data processing method for controlling Kalman filter instability[J]. Technical Acoustics, 2008(5): 761-764.
[16]	张晓飞, 辛明真, 隋海琛, 等. 基于交互式多模型卡尔曼滤波的AUV超短基线跟踪算法[J]. 水下无人系统学报, 2022, 30(1): 29-36. Zhang Xiaofei, Xin Mingzhen, Sui Haichen. AUV ultra-short baseline tracking algorithm based on interactive multi-model Kalman filter[J]. Journal of Unmanned Undersea Systems, 2022, 30(1): 29-36.
[17]	朱红运, 苗岩松, 庞建国. 基于卡尔曼滤波的遥测数据野值剔除方法[J]. 航天返回与遥感, 2021, 42(4): 137-143. Zhu Hongyun, Miao Yansong, Pang Jianguo. An outliers elimination method of telemetry data based on Kalman filter[J]. Spacecraft Recovery & Remote Sensing, 2021, 42(4): 137-143.
[18]	曹玉波, 李健, 周琦祥, 等. 一阶惯性数字化滤波算法研究及应用[J]. 吉林化工学院学报, 2019, 36(11): 50-52. Cao Yubo, Li Jian, Zhou Qixiang, et al. Research and application on digital filter algorithm of first order inertial system[J]. Journal of Jilin Institute of Chemical Technology, 2019, 36(11): 50-52.