Internal leakage detection for feedwater heaters in power plants using neural networks

Abstract

As interest in safety and performance of power plants becomes more serious and wide-ranging, the significance of research on turbine cycles has attracted more attention. This paper particularly focuses on thermal performance analysis under the conditions of internal leakages inside closed-type feedwater heaters (FWHs) and their diagnosis to identify the locations and to quantify leak rates. Internal leakage is regarded as flow movement through the isolated path but remaining inside the system boundary of a turbine cycle. For instance, leakages through the cracked tubes, tube-sheets, or pass partition plates in a FWH are internal leakages. Internal leakages impact not only plant efficiency, but also direct costs and/or even plant safety associated with the appropriate repairs. Some types of internal leakages are usually critical to get the parts fixed and back in a timely manner. The FWHs installed in a Korean standard nuclear power plant were investigated in this study. Three technical steps have been, then, conducted: (1) the detailed modeling of FWHs covering the leakage from tubes, tube-sheets, or pass partition plates using the simulation model, (2) thermal performance analysis under various leakage conditions, and (3) the development of a diagnosis model using a feed-forward neural network, which is the correlation between thermal performance indices and leakage conditions. Since the operational characteristics of FWHs are coupled with one another and/or with other neighbor components such as turbines or condensers, recognizing internal leakages is difficult with only an analytical model and instrumentation at the inlet and outlet of tube- and shell-sides. The proposed neural network-based correlation was successfully validated for test cases.