Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2012-01-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/5/1/101/ |
| _version_ | 1798024549639389184 |
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| author | Yea-Kuang Chan Jyh-Cherng Gu |
| author_facet | Yea-Kuang Chan Jyh-Cherng Gu |
| author_sort | Yea-Kuang Chan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T18:04:28Z |
| format | Article |
| id | doaj.art-a7014f83f2854c1cbc0768ad60c623fa |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T18:04:28Z |
| publishDate | 2012-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a7014f83f2854c1cbc0768ad60c623fa2022-12-22T04:10:22ZengMDPI AGEnergies1996-10732012-01-015110111810.3390/en5010101Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power PlantsYea-Kuang ChanJyh-Cherng GuDue 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.http://www.mdpi.com/1996-1073/5/1/101/adaptive neuro-fuzzy inference system (ANFIS)neural networkturbine cycleturbine-generatornuclear power plant |
| spellingShingle | Yea-Kuang Chan Jyh-Cherng Gu Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants Energies adaptive neuro-fuzzy inference system (ANFIS) neural network turbine cycle turbine-generator nuclear power plant |
| title | Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants |
| title_full | Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants |
| title_fullStr | Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants |
| title_full_unstemmed | Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants |
| title_short | Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants |
| title_sort | modeling of turbine cycles using a neuro fuzzy based approach to predict turbine generator output for nuclear power plants |
| topic | adaptive neuro-fuzzy inference system (ANFIS) neural network turbine cycle turbine-generator nuclear power plant |
| url | http://www.mdpi.com/1996-1073/5/1/101/ |
| work_keys_str_mv | AT yeakuangchan modelingofturbinecyclesusinganeurofuzzybasedapproachtopredictturbinegeneratoroutputfornuclearpowerplants AT jyhchernggu modelingofturbinecyclesusinganeurofuzzybasedapproachtopredictturbinegeneratoroutputfornuclearpowerplants |