Application of Structural Analysis to Improve Fault Diagnosis in a Gas Turbine

Abstract

This chapter deals with the fault diagnosis issues for a Gas Turbine, GT, of a Combined Cycle Power Plant, CCPP, considering diverse fault scenarios. The essential and more critical component in the plant self is the gas turbine, because it comprises complex dynamical subsystems which can fail due to faults in sensors, actuators and components and relies heavily on the control system affecting the reliability, availability and maintainability of the power plant. This issue motivated this research work oriented to design a diagnosis system by software for gas turbines of electric power plants. The key for a faults diagnosis system is the discrepancy between expected and actual behavior and this can be identified, on real time only if redundant information between the process variables is available (Frank, 1990). Artificial Intelligence and Control communities have developed methods to generate symptoms or signals by software, called residuals, which reflect the discrepancies in faults conditions Venkatasubramanian et al. (2003a), Venkatasubramanian et al. (2003c), Venkatasubramanian et al. (2003d). However previous to the residual generators design it is necessary and essential to determine which data requirements are required to solve a specific fault diagnosis issue. To analyze under which conditions faults in sensors and actuators of a GT can be detected and isolated, the structural properties of the model are used here. The redundancy of the structure is studied using graph tools for the subsystems of the GT considering the available measurements. A non-linear complex dynamic model of the GT given by 37 algebraic and differential equations is considered to identify the required redundancy degrees for diverse fault scenarios of the units without numerical values. As result of the generic analysis, 10 relations are obtained which allow to detect faults in all components of the gas turbine unit. The rotors mechanical coupling to gas turbine unit for one side and the electric generator unit for the other side, is identified as a subsystem in which faults are undetectable and then, a diagnosis system for this subsystem is not feasible. This means, the standard instrumentation of the GT restricts its performance from safety and integrity point of view. On the base of this result and using the redundant graph concept (Verde & Mina, 2008), it is suggested here to add a sensor to increase the redundance and consequently to improve the fault detectability of the turbogenerator in the presence of mechanical and sensors faults. This is the main contribution of the work. The implementation of redundant graphs with specific simulated data of a GT validates this statement.