Model Based Diagnosis and Supervision of Industrial Gas Turbines

• Main Author: Larsson, Emil
• Format: Doctoral Thesis
• Language: English
• Published: Linköpings universitet, Fordonssystem 2014
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-106256; http://nbn-resolving.de/urn:isbn:978-91-7519-312-0 (print)

Supervision of performance in gas turbine applications is important in order to achieve: (i) reliable operations, (ii) low heat stress in components, (iii) low fuel consumption, and (iv) efficient overhaul and maintenance. To obtain good diagnosis performance it is important to have tests which are based on models with high accuracy. A main contribution of the thesis is a systematic design procedure to construct a fault detection and isolation (FDI) system which is based on complex nonlinear models.These models are preliminary used for simulation and performance evaluations. Thus, is it possible to use thesemodels also in the FDI-system and whichmodel parts are necessary to consider in the test design? To fulfill the requirement of an automated design procedure, a thermodynamic gas turbine package GTLib is developed. Using the GTLib framework, a gas turbine diagnosismodel is constructed where component deterioration is introduced. In the design of the test quantities, equations from the developed diagnosis models are carefully selected.These equations are then used to implement a Constant Gain Extended Kalman filter (CGEKF) based test quantity.The number of equations and variables which the test quantity is based on is significantly reduced compared to the original reference model.The test quantity is used in the FDI-system to supervise the performance and the turbine inlet temperature which is used in the controller. An evaluation is performed using experimental data from a gas turbine site.The case study shows that the designed FDI-system can be used when the decision about a compressor wash is taken. When the FDI-system is augmented with more test quantities it is possible to diagnose sensor and actuator faults at the same time the performance is supervised. Slow varying sensor and actuator bias faults are difficult diagnose since they appear in a similar manner as the performance deterioration, but the FDI-system has the ability to detect these faults. Finally, the proposed model based design procedure can be considered when an FDI-system of an industrial gas turbine is constructed.