Process fault detection in heat recovery steam generator using an Artificial Neural Network simplification of a dynamic first principles model

Abstract

A combined cycle power plant (CCPP) is a complex system with a Gas Turbine, Steam Turbine and a Heat Recovery Steam Generator (HRSG) working together. These three units work together and make the process highly interdependent. The onset of any fault in one of the above units would results in a significant reduction in overall efficiency and potentially lead to catastrophic accidents. Such failures can occur due to process faults because of large abrupt variations of operating conditions and structural faults due to corrosion, uneven stresses due to frequent cyclic operations. Conventionally, the identification of such leakage locations is made via visual inspection which is a time consuming and tedious. In the present work, we discuss a fault diagnosis strategy for an actual industrial HRSG present in a CCPP. Various steady state models at different loads of CCPP as well as a dynamic model are developed. Various structural faults in the form of leakages are incorporated in the heat exchangers. An Artificial Neural Network (ANN) model is developed based on data from the above simulations to detect the leaking heat exchangers.