Fast leak detection and location of gas pipelines based on an adaptive particle filter

• Autorzy: Ming Liu , Shu Zang , Donghua Zhou
• Języki publikacji: EN

Abstrakty

EN

Leak detection and location play an important role in the management of a pipeline system. Some model-based methods, such as those based on the extended Kalman filter (EKF) or based on the strong tracking filter (STF), have been presented to solve this problem. But these methods need the nonlinear pipeline model to be linearized. Unfortunately, linearized transformations are only reliable if error propagation can be well approximated by a linear function, and this condition does not hold for a gas pipeline model. This will deteriorate the speed and accuracy of the detection and location. Particle filters are sequential Monte Carlo methods based on point mass (or ``particle'') representations of probability densities, which can be applied to estimate states in nonlinear and non-Gaussian systems without linearization. Parameter estimation methods are widely used in fault detection and diagnosis (FDD), and have been applied to pipeline leak detection and location. However, the standard particle filter algorithm is not applicable to time-varying parameter estimation. To solve this problem, artificial noise has to be added to the parameters, but its variance is difficult to determine. In this paper, we propose an adaptive particle filter algorithm, in which the variance of the artificial noise can be adjusted adaptively. This method is applied to leak detection and location of gas pipelines. Simulation results show that fast and accurate leak detection and location can be achieved using this improved particle filter.

Słowa kluczowe

EN

particle filter; gas pipeline; leak detection and location

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2005
• Tom: 15
• Numer: 4
• Strony: 541-550

Daty

wydano

2005

otrzymano

2004-10-01

poprawiono

2005-07-21

(nieznana)

2005-09-10

Twórcy

autor

Ming Liu

• Department of Automation, Tsinghua University, Beijing 100084, China

autor

Shu Zang

• Zhen Jiang Watercraft College, Zhenjiang 212003, China

autor

Donghua Zhou

• Department of Automation, Tsinghua University, Beijing 100084, China

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