Modeling of Turbine Cycle Using Neuro-fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants

Abstract

Due to the very complex sets of component systems, interrelated thermodynamic processes and seasonal change in operating conditions, it is relatively difficult to find a suitable model for turbine cycle of nuclear power plants (NPPs). This paper deals with the modeling of turbine cycle to predict turbine-generator output using an adaptive neuro-fuzzy inference system (ANFIS) for Unit 1 of Kuosheng NPP in Taiwan. Plant operating data obtained from Kuosheng NPP between 2006 and 2011 were verified using a linear regression model with a 95% confidence interval. The key parameters of turbine cycle including turbine throttle pressure, condenser backpressure, feed water flow rate and final feed water temperature are selected as inputs for the ANFIS based turbine cycle model. In addition, a thermodynamic turbine cycle model was developed using commercial software, PEPSE, to compare the performance of the ANFIS based turbine cycle model. The results show that the proposed ANFIS based turbine cycle model is capable of accurately estimating turbine-generator output and providing more reliable results than the PEPSE based turbine cycle models Moreover, test results show that the ANFIS performed better than the artificial neural network (ANN), which has also being tried to model the turbine cycle. The results of this study provide an alternative approach to evaluate the thermal performance of nuclear power plants.