State Vector Identification of Hybrid Model of a Gas Turbine by Real-Time Kalman Filter
Gustavo Delgado-Reyes,
Pedro Guevara-Lopez,
Igor Loboda,
Leobardo Hernandez-Gonzalez,
Jazmin Ramirez-Hernandez,
Jorge-Salvador Valdez-Martinez,
Asdrubal Lopez-Chau
Affiliations
Gustavo Delgado-Reyes
Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacan, Instituto Politécnico Nacional, Av. Santa Ana No. 1000, Col. San Francisco Culhuacan, Mexico City C.P. 04430, Mexico
Pedro Guevara-Lopez
Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacan, Instituto Politécnico Nacional, Av. Santa Ana No. 1000, Col. San Francisco Culhuacan, Mexico City C.P. 04430, Mexico
Igor Loboda
Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacan, Instituto Politécnico Nacional, Av. Santa Ana No. 1000, Col. San Francisco Culhuacan, Mexico City C.P. 04430, Mexico
Leobardo Hernandez-Gonzalez
Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacan, Instituto Politécnico Nacional, Av. Santa Ana No. 1000, Col. San Francisco Culhuacan, Mexico City C.P. 04430, Mexico
Jazmin Ramirez-Hernandez
Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacan, Instituto Politécnico Nacional, Av. Santa Ana No. 1000, Col. San Francisco Culhuacan, Mexico City C.P. 04430, Mexico
Jorge-Salvador Valdez-Martinez
Universidad Tecnológica Emiliano Zapata del Estado de Morelos, Universidad Tecnológica No. 1, Morelos 62760, Mexico
Asdrubal Lopez-Chau
Universidad Autónoma del Estado de México, CU UAEM Zumpango Kilómetro 3.5 Camino Viejo a Jilotzingo, Estado de Mexico 55600, Mexico
A model and real-time simulation of a gas turbine engine (GTE) by real-time tasks (RTT) is presented. A Kalman filter is applied to perform the state vector identification of the GTE model. The obtained algorithms are recursive and multivariable; for this reason, ANSI C libraries have been developed for (a) use of matrices and vectors, (b) dynamic memory management, (c) simulation of state-space systems, (d) approximation of systems using equations in matrix finite difference, (e) computing the mean square errors vector, and (f) state vector identification of dynamic systems through digital Kalman filter. Simulations were performed in a Single Board Computer (SBC) Raspberry Pi 2® with a real-time operating system. Execution times have been measured to justify the real-time simulation. To validate the results, multiple time plots are analyzed to verify the quality and convergence time of the mean square error obtained.
