基于改进无迹卡尔曼滤波的油气管道定位方法

1.清华大学电机系;2.中油管道检测技术有限责任公司;3.清华大学新型电力系统运行与控制全国重点实验室

管道;定位;误差模型;无迹卡尔曼滤波;平滑滤波;直管;弯管

Oil and gas pipeline locating method based on improved Unscented Kalman Filter
ZHANG Jinghua1,WANG Xinrao2,SHI Ruilong2,JIANG Kunling2,DONG Tianyu2,PENG Lisha1,HUANG Songling1,3

1.Department of Electrical Engineering, Tsinghua University; 2.China Petroleum Pipeline Inspection Technologies Co. Ltd.; 3.State Key Laboratory of Power System Operation and Control, Tsinghua University

pipeline, localization, error model, Unscented Kalman Filter (UKF), smoothing filter, straight pipe, curved pipe

DOI: 10.6047/j.issn.1000-8241.2024.03.007

备注

【目的】部分老龄油气长输管道的运行年限较长,存在具体地理位置信息缺失、实际管道定位与初始设计路线不一致等问题,而传统油气管道定位方法的累积误差大、定位精度低。【方法】为了提高油气管道的定位精度,提出一种基于改进无迹卡尔曼滤波(Unscented Kalman Filter,UKF)的油气管道定位方法:以陀螺仪与加速度计组成的惯性测量单元为主要设备,利用里程轮、地面标记器作为辅助定位设备,将管道检测器的姿态、速度及位置误差作为状态量,再将里程轮输出的速度与加速度计解算的姿态信息作为观测量,建立非线性捷联惯导系统误差传递模型;利用改进UKF估计系统误差传递模型,降低噪声对定位精度的影响,并结合前向与后向平滑滤波方法、直管与弯管识别方法修正管道检测器的角速度和加速度信息,进一步降低系统解算过程的累积误差。【结果】通过数值模拟与物理试验验证结果表明,所建立的油气管道定位系统可减小长输管道定位的累积误差,对管道所在具体位置进行定位的最大误差为0.56%,在测试环境保持不变的情况下,与传统卡尔曼滤波(Kalman Filter,KF)算法、UKF算法相比,改进UKF算法的定位精度分别提高了约34.2%、30.2%。【结论】基于改进UKF的油气管道定位方法减小了系统解算过程的累积误差,提高了管道的定位精度,在油气长输管道定位中具有较高的实用价值。(图6表1,参[21]
[Objective] Part of oil and gas pipelines in China, which have been in operation for a long time, are facing problems such as the lack of specific geographic information and deviation of the pipeline from the designed laying location. However, traditional pipeline localization methods possess limitations of significant cumulative errors and low accuracy. [Methods] To enhance the precision of oil and gas pipeline localization, a new method was proposed based on improved Unscented Kalman Filter (UKF) algorithm. The primary equipment, which is an inertial measurement unit consisting of a gyroscope and an accelerometer, supplemented by a wheel odometer and ground markers as auxiliary locator devices was utilized in this method. The attitude, velocity, and position errors of the pipeline detector were taken as state variables, and the speed of the wheel odometer and attitude information obtained from the solution of accelerometer data were taken as observables, to establish a nonlinear error propagation model of strap-down inertial navigation system. An improved UKF algorithm was employed to estimate the error transfer model for reducing the impact of noise on localization accuracy. The angular velocity and acceleration information were corrected by combining forward-backward smoothing filter method and straight and curved pipe identification method, to further reduce cumulative errors in the system solution process. [Results] Numerical simulations and physical experiments revealed that the proposed oil and gas pipeline localization system has significantly reduced cumulative errors in locating long-distance pipelines. The maximum error in determining the specific location of oil and gas pipelines was found to be 0.56%. The UKF algorithm, which was proposed in this paper, showed a locating accuracy level of approximately 34.2% higher compared to the traditional Kalman Filter (KF) algorithm and 30.2% higher compared to the UKF algorithm under constant testing conditions. [Conclusion] The proposed oil and gas pipeline locating method based on an improved UKF presents high practical value for long-distance pipeline localization as it has lower cumulative errors in the system solution process and higher accuracy in pipeline localization. (6 Figures, 1 Table, 21 References)
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