王吉, 杜泽明, 鲁伟. 有障碍空间天然气管道泄漏源改进粒子群定位算法[J]. 油气储运, 2022, 41(8): 946-952. DOI: 10.6047/j.issn.1000-8241.2022.08.010
引用本文: 王吉, 杜泽明, 鲁伟. 有障碍空间天然气管道泄漏源改进粒子群定位算法[J]. 油气储运, 2022, 41(8): 946-952. DOI: 10.6047/j.issn.1000-8241.2022.08.010
WANG Ji, DU Zeming, LU Wei. Modified particle swarm algorithm for locating leakage source on natural gas pipeline in obstructed space[J]. Oil & Gas Storage and Transportation, 2022, 41(8): 946-952. DOI: 10.6047/j.issn.1000-8241.2022.08.010
Citation: WANG Ji, DU Zeming, LU Wei. Modified particle swarm algorithm for locating leakage source on natural gas pipeline in obstructed space[J]. Oil & Gas Storage and Transportation, 2022, 41(8): 946-952. DOI: 10.6047/j.issn.1000-8241.2022.08.010

有障碍空间天然气管道泄漏源改进粒子群定位算法

Modified particle swarm algorithm for locating leakage source on natural gas pipeline in obstructed space

  • 摘要: 为引导搜索机器人快速准确定位复杂有障碍空间内天然气管道的泄漏源,基于保守收敛粒子群算法和人工势场法分别在协同搜索、避障方面的优势,提出了融合两种算法的改进型粒子群算法(Modified Guaranteed Convergence Particle Swarm Optimization,MGCPSO)来定位泄漏源。构建泄漏扩散场模拟包含障碍物的二维空间泄漏浓度场,对MGCPSO算法进行定位能力测试,并分析传感器测量误差和机器人数量对算法定位能力的影响。结果表明:MGCPSO算法可以定位有圆形障碍物空间内的单个泄漏源;增大测量误差将导致算法预测的泄漏源位置偏差增大,当测量误差的标准差小于0.1时,该算法定位结果准确;增加机器人数量能够提高算法的预测精度。MGCPSO算法能够指导机器人在搜索泄漏源的过程中躲避障碍物,可为现场应急救援提供数据支持,未来应进一步研究算法在小种群条件下的定位精度。

     

    Abstract: In order to guide the search robots to locate quickly and accurately the leakage source on natural gas pipeline in the complex obstructed space, a Modified Guaranteed Convergence Particle Swarm Optimization (MGCPSO) algorithm integrating the guaranteed convergence particle swarm optimization and the artificial potential field was proposed based on their respective advantages in collaborative search and obstacle avoidance. Specifically, a leakage diffusion field was created to simulate the two-dimensional leakage concentration field considering obstacles, the locating capability of MGCPSO was tested, and the influence of measuring error and robot number on the locating capability was analyzed. The results indicate that the MGCPSO algorithm can locate the single leakage source in the space with circular obstacle, and the increase of measuring error can lead to the increasing in location deviation of the leakage source predicted by the algorithm. The locating result of the algorithm is accurate when the standard deviation of measuring error is less than 0.1. In addition, increasing the robot number can improve the prediction accuracy of the algorithm. Generally, the MGCPSO algorithm could guide the robots to avoid the obstacles during the searching of leakage source and provide data support for the field emergency rescue. The locating accuracy of the algorithm under small population conditions should be further studied in the future.

     

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