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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2011

Autores

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

Revista

Título: IEEE Transactions on Control Systems Technology

Vol. 19 Nº 1

Páginas: 77-92

ISSN: 1063-6536

Editora: IEEE

Indexação

ISI Web of Knowledge - 22 Citações

ISI Web of Science

Scopus - 36 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16