Developing a turbine cycle model using adaptive neuro-fuzzy inference system for Kuosheng nuclear power plant

Abstract

The objective of this study is to develop a turbine cycle model using the adaptive neuro-fuzzy inference system (ANFIS) for the Kuosheng nuclear power plant (NPP) in Taiwan. This ANFIS-based turbine cycle model is used to estimate the turbine–generator output. The plant operating data were verified using a linear regression model with a corresponding 95% confidence interval for the operating data. In this study, the key parameters were selected as inputs for the neuro-fuzzy-based turbine cycle model. After training and validating with key parameters, including turbine throttle pressure, condenser backpressure, feedwater flow rate, and final feedwater temperature, the proposed model was used to estimate the turbine–generator output. The effectiveness of the proposed ANFIS-based turbine cycle model was demonstrated using plant operating data obtained from Unit 1 of the Kuosheng NPP owned by Taiwan Power Company. The results show that this neuro-fuzzy-based turbine cycle model can be used to accurately estimate the turbine–generator output. In addition, a thermodynamic turbine cycle model was developed using a commercial software, PEPSE, in order to compare the performance of the ANFIS-based turbine cycle model. The results show that the proposed neuro-fuzzy-based turbine cycle model is capable of accurately estimating the turbine–generator output and providing more reliable results than the PEPSE turbine cycle model, with regard to estimation accuracy and clearly defined trends. The results of this study provide an alternative approach for evaluating the thermal performance of NPPs.