Next Article in Journal
Performance Analysis and Simulation of a Novel Brushless Double Rotor Machine for Power-Split HEV Applications
Next Article in Special Issue
Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry
Previous Article in Journal
Correlation of the Growth Rate of the Hydrate Layer at a Guest/Liquid-Water Interface to Mass Transfer Resistance
Previous Article in Special Issue
Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models
Energies 2012, 5(1), 101-118; doi:10.3390/en5010101
Article

Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants

1,2,*  and 1
Received: 15 December 2011; in revised form: 16 January 2012 / Accepted: 16 January 2012 / Published: 19 January 2012
(This article belongs to the Special Issue Intelligent Energy Demand Forecasting)
Download PDF [2587 KB, uploaded 19 January 2012]
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 an accurate model for turbine cycle of nuclear power plants (NPPs). This paper deals with the modeling of turbine cycles to predict turbine-generator output using an adaptive neuro-fuzzy inference system (ANFIS) for Unit 1 of the Kuosheng NPP in Taiwan. Plant operation 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, feedwater flow rate and final feedwater temperature are selected as inputs for the ANFIS based turbine cycle model. In addition, a thermodynamic turbine cycle model was developed using the 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 effectiveness of the proposed neuro-fuzzy based turbine cycle model was demonstrated using the actual operating data of Kuosheng NPP. Furthermore, the results also provide an alternative approach to evaluate the thermal performance of nuclear power plants.
Keywords: adaptive neuro-fuzzy inference system (ANFIS); neural network; turbine cycle; turbine-generator; nuclear power plant adaptive neuro-fuzzy inference system (ANFIS); neural network; turbine cycle; turbine-generator; nuclear power plant
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Chan, Y.-K.; Gu, J.-C. Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants. Energies 2012, 5, 101-118.

AMA Style

Chan Y-K, Gu J-C. Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants. Energies. 2012; 5(1):101-118.

Chicago/Turabian Style

Chan, Yea-Kuang; Gu, Jyh-Cherng. 2012. "Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants." Energies 5, no. 1: 101-118.


Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert