An LPV Modeling and Identification Approach to Leakage Detection in High Pressure Natural Gas Transportation Networks

• Type: Article in International Scientific Journal
• Date: 2011

Authors

P. Lopes dos Santos (Author) FEUP; Teresa-P Azevedo-Perdicoúlis (Author) Other; José A. Ramos (Author) Other; J. L. Martins de Carvalho (Author) FEUP; J. Milhinhos (Author) Other

Journal

Title: IEEE Transactions on Control Systems Technology

Vol. 19 No. 1

Pages: 77-92

ISSN: 1063-6536

Publisher: IEEE

Indexing

ISI Web of Knowledge - 22 Citations

ISI Web of Science

Scopus - 36 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-002-YQ4

DOI: 10.1109/tcst.2010.2077293

Abstract (EN): In this paper a new approach to gas leakage detection in high pressure natural gas transportation networks is proposed. The pipeline is modelled as a Linear Parameter Varying (LPV) System driven by the source node massflow with the gas inventory variation in the pipe (linepack variation, proportional to the pressure variation) as the scheduling parameter. The massflow at the offtake node is taken as the system output. The system is identified by the Successive Approximations LPV System Subspace Identification Algorithm which is also described in this paper. The leakage is detected using a Kalman filter where the fault is treated as an augmented state. Given that the gas linepack can be estimated from the massflow balance equation, a differential method is proposed to improve the leakage detector effectiveness. A small section of a gas pipeline crossing Portugal in the direction South to North is used as a case study. LPV models are identified from normal operational data and their accuracy is analyzed. The proposed LPV Kalman filter based methods are compared with a standard mass balance method in a simulated 10% leakage detection scenario. The Differential Kalman Filter method proved to be highly efficient.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16