The supervision of performance in gas turbine applications is crucial 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 a good diagnosis of performance it is important to have tests which are based on models with high accuracy. A main contribution is a systematic design procedure to construct a fault detection and isolation (FDI) system for complex nonlinear models. To fulfill the requirement of an automated design procedure, a thermodynamic gas turbine package (GTLib) is developed. Using the GTLib framework, a gas turbine diagnosis model is constructed where component deterioration is introduced. In the design of the test quantities, equations from the developed diagnosis model are carefully selected. These equations are then used to implement a constant gain extended Kalman filter (CGEKF)-based test quantity. The test quantity is used in the FDI-system to supervise the performance and in the controller to estimate the flame temperature. 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. Thus, the proposed model-based design procedure can be considered when an FDI-system of an industrial gas turbine is constructed.

Model based diagnosis and supervision of industrial gas turbines are studied. Monitoring of an industrial gas turbine is important as it gives valuable information for the customer about service performance and process health. The overall objective of the paper is to develop a systematic procedure for modelling and design of a model based diagnosis system, where each step in the process can be automated and implemented using available software tools. A new Modelica gas media library is developed, resulting in a significant model size reduction compared to if standard Modelica components are used. A systematic method is developed that, based on the diagnosis model, extracts relevant parts of the model and transforms it into a form suitable for standard observer design techniques. This method involves techniques from simulation of DAE models and a model reduction step. The size of the final diagnosis model is 20% of the original model size. Combining the modeling results with fault isolation techniques, simultaneous isolation of sensor faults and fault tolerant health parameter estimation is achieved.

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 these models also in the FDI-system and which model 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 diagnosis model 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.iii Populärvetenskaplig sammanfattning Diagnostik och prestandaövervakning förekommer inom många industriella applikationer. Detta område är viktigt att beakta för att: (i) upprätthålla hög tillförlitlighet, (ii) undvika onödig belastning på komponenter, (iii) minimera energiförbrukningen, och (iv) effektivt kunna planera underhåll. Eftersom prestandan i en applikation oftast inte är direkt mätbar behövs metoder för att kunna skatta dessa prestandaparametrar utifrån kända mätsignaler. Detta kan vara svårt eftersom: (i) sambandet mellan mätsig-naler och prestandaparametrar kan vara komplicerat, (ii) mätsignaler innehåller brus, och (iii) mätsignaler kan vara opålitliga och visa ett felaktigt värde. Dessa aspekter bör beaktas när ett diagnos-och övervakningssystem utvecklas. Eftersom många system är komplexa kan det vara nödvändigt att ha effektiva och automatiserade metoder för att skatta prestanda och bestämma diag...

Monitoring of an industrial gas turbine is important since it gives valuable information for the customer about maintenance, performance and process health. The objective of the paper is to develop a monitoring system for an industrial gas turbine application with a model based diagnosis approach. A constant gain extended Kalman observer is developed. The observer compensates for different ambient conditions such as pressure, temperature and relative humidity, due to the amount of water in the atmosphere. The developed observer, extended with seven health parameters, is automatically constructed from the diagnosis model. These health parameters shall capture deviations in some of the gas path performance parameters such as efficiency, mass flow, turbine inlet area and head loss. The constructed observer is evaluated through a simulation study where the ambient conditions are changed. The considered observer capture the change in different ambient conditions nearly perfect. An observer that does not compensate for different ambient conditions gives an error for about 1–2% for the considered health parameters for the given test case. The constructed observer is also evaluated on measurement data from a mechanical drive site. A degradation in efficiency and mass flow for the compressor due to fouling can be seen in the estimations. After the compressor wash is performed, the degradations for the compressor are partially restored by about 2% which can be seen in the considered health parameters.

Supervision of the performance of an industrial gas turbine is important since it gives valuable information of the process health and makes efficient determination of compressor wash intervals possible. Slowly varying sensor faults can easily be misinterpreted as performance degradations and result in an unnecessary compressor wash. Here, a diagnostic algorithm is carefully combined with non-linear state observers to achieve fault tolerant performance estimation. The proposed approach is evaluated in an experimental case study with six months of measurement data from a gas turbine site. The investigation shows that faults in all gas path instrumentation sensors are detectable and isolable. A key result of the case study is the ability to detect and isolate a slowly varying sensor fault in the discharge temperature sensor after the compressor. The fault is detected and isolated before the wash condition of the compressor is triggered, resulting in fault tolerant estimation of compressor health parameters.