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Yea-Kuang Chan
Nuclear Engineering Division, Institute of Nuclear Energy Research, No. 1000, Wenhua Rd., Jiaan Village, Longtan District, Taoyuan City 32546, Taiwan, ROC

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Short communication
Published: 10 January 2019 in Nuclear Engineering and Design
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This process of requesting operation at thermal power levels above the current licensed power level for nuclear power station is referred to as a Power Uprate (PU). There are many nuclear power stations around the world that have implemented power uprate operation for improving their efficiency and economic performance. The Chinshan Nuclear Power Station (CNPS) performed the 1.66% and 2% power uprate for MURPU and SPU, respectively. After successfully achieving the operation of power uprates, the total increase of electrical output for CNPS has amounted to 35.79 MWe. In addition, it also reaches the reduction of CO2 emission about 0.68 million tons per year compare to natural gas combined cycle (NGCC) power plant. Furthermore, the net increase in yearly revenue was compared to the cost of implementing the MURPU and SPU, and the payback time was less than one year. In addition to the calculation of thermal power uncertainty, the power ascension test results, major operating parameters after power uprates, cost benefit analysis, and calculation of CO2 reduction are also presented in this paper. Moreover, the key operating parameters of the turbine cycle measured after power uprate have established a reference base for monitoring station operating performance and provide useful information to turbine cycle design for nuclear power plant.

ACS Style

Yea-Kuang Chan; Yu-Ching Tsai. Power uprate operation at Chinshan Nuclear Power Station. Nuclear Engineering and Design 2019, 343, 96 -102.

AMA Style

Yea-Kuang Chan, Yu-Ching Tsai. Power uprate operation at Chinshan Nuclear Power Station. Nuclear Engineering and Design. 2019; 343 ():96-102.

Chicago/Turabian Style

Yea-Kuang Chan; Yu-Ching Tsai. 2019. "Power uprate operation at Chinshan Nuclear Power Station." Nuclear Engineering and Design 343, no. : 96-102.

Journal article
Published: 01 March 2018 in Energy Conversion and Management
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To respond to the global climate change, Taiwan has announced an ambitious target for the development of renewable energy, which is characterized by solar power 20 GW and wind power 4.2 GW by 2025, but the intermittency of renewable energy sources might have serious impacts on the existing power grid. Not only the energy system but also water resources will be impacted by the global climate change. In Taiwan, the strength of rainfall increases but the frequency of rain decreases; this factor combined with a disadvantageous topography to store rainfall worsens the water-shortage issues. As a solution of the aforementioned issues related to the renewable energy sources and water resources concurrently, an integrated system and its operating model for renewable energy sources and water resources are proposed according to which hydropower, pumped-storage hydropower, solar power, wind power, desalination plants and the conjunctive use of water between two reservoirs are considered. A mathematical model is established to describe how the system works under various input data. The results show that, with a retrofit of existing old units and the addition of 102-MW new units, the hydropower unit of the proposed system can eliminate a requirement of 853-MW gas-fired power plants during peak loading in the reference case; the cost, US$45 million per year, of power generation can be saved. With 1099-MW pumped-storage hydropower units added, the proposed system and its operating model further enhance the peak-loading support; relative to a battery-storage system in the reference case, the cost of energy storage can save US$166 million per year. As for the desalination plants in the proposed system, the cost of producing water still exceeds that of the planned reservoir in the reference case because of its greater cost of operation. On considering the total benefit from the water and energy sector, the extra expense, US$41 million per year, for desalination can, however, be readily compensated; the proposed system can save more, US$171 million per year, than the reference case.

ACS Style

Yu-Ching Tsai; Yea-Kuang Chan; Fu-Kuang Ko; Jing-Tang Yang. Integrated operation of renewable energy sources and water resources. Energy Conversion and Management 2018, 160, 439 -454.

AMA Style

Yu-Ching Tsai, Yea-Kuang Chan, Fu-Kuang Ko, Jing-Tang Yang. Integrated operation of renewable energy sources and water resources. Energy Conversion and Management. 2018; 160 ():439-454.

Chicago/Turabian Style

Yu-Ching Tsai; Yea-Kuang Chan; Fu-Kuang Ko; Jing-Tang Yang. 2018. "Integrated operation of renewable energy sources and water resources." Energy Conversion and Management 160, no. : 439-454.

Journal article
Published: 27 July 2017 in Nuclear Technology
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ACS Style

Yea-Kuang Chan. Thermal Performance Test on Nuclear Power Station: A Case Study in Taiwan. Nuclear Technology 2017, 200, 80 -92.

AMA Style

Yea-Kuang Chan. Thermal Performance Test on Nuclear Power Station: A Case Study in Taiwan. Nuclear Technology. 2017; 200 (1):80-92.

Chicago/Turabian Style

Yea-Kuang Chan. 2017. "Thermal Performance Test on Nuclear Power Station: A Case Study in Taiwan." Nuclear Technology 200, no. 1: 80-92.

Journal article
Published: 16 March 2017 in Kerntechnik
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The objective of this study is to develop a turbine cycle model using the multiple regression approach to estimate the turbine-generator output for the Chinshan Nuclear Power Plant (NPP). The plant operating data was verified using a linear regression model with a corresponding 95 percnt; confidence interval for the operating data. In this study, the key parameters were selected as inputs for the multiple regression based turbine cycle model. The proposed model was used to estimate the turbine-generator output. The effectiveness of the proposed turbine cycle model was demonstrated by using plant operating data obtained from the Chinshan NPP Unit 2. The results show that this multiple regression based turbine cycle model can be used to accurately estimate the turbine-generator output. In addition, this study also provides an alternative approach with simple and easy features to evaluate the thermal performance for nuclear power plants.

ACS Style

Yea-Kuang Chan; Yu-Ching Tsai. Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant. Kerntechnik 2017, 82, 24 -30.

AMA Style

Yea-Kuang Chan, Yu-Ching Tsai. Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant. Kerntechnik. 2017; 82 (1):24-30.

Chicago/Turabian Style

Yea-Kuang Chan; Yu-Ching Tsai. 2017. "Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant." Kerntechnik 82, no. 1: 24-30.

Articles
Published: 08 June 2015 in Journal of the Chinese Institute of Engineers
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The purpose of the performance test for Unit 1 of Maanshan nuclear power plant was to determine the electrical output and heat rate after the retrofit of the high-pressure turbine during the refueling in 2012. The performance test was conducted in order to verify that the actual improvement in electrical output resulting from the replacement of the high-pressure turbine meets the vendor’s guarantee. A total of two performance test runs was conducted in accordance with the American Society of Mechanical Engineers performance test code (PTC) 6. The measured electrical powers for the two test runs were 977.4 and 975.0 MWe, respectively, and the average value was 976.2 MWe. After correcting the electrical power to the rated conditions specified in the performance test procedure, the gross electric output was 983.2 MWe. The corrected heat rate for the two performance tests were 10365 and 10353 kJ/kWh, respectively. The deviation between two corrected heat rates was 0.11%. Since the acceptable deviation between two test runs required by PTC 6 is no more than 0.25%, the quality of test results is acceptable. Moreover, the performance test results also demonstrated that the improvement in gross electrical output was 17.6 MWe, which was higher than the contract guarantee of 10.0 MWe.

ACS Style

Yea-Kuang Chan; Yu-Ching Tsai; Chin-Jang Chang; Ping-Ling Hsieh. Performance test and analysis after high pressure turbine retrofit for Maanshan nuclear power plant Unit 1. Journal of the Chinese Institute of Engineers 2015, 38, 918 -927.

AMA Style

Yea-Kuang Chan, Yu-Ching Tsai, Chin-Jang Chang, Ping-Ling Hsieh. Performance test and analysis after high pressure turbine retrofit for Maanshan nuclear power plant Unit 1. Journal of the Chinese Institute of Engineers. 2015; 38 (7):918-927.

Chicago/Turabian Style

Yea-Kuang Chan; Yu-Ching Tsai; Chin-Jang Chang; Ping-Ling Hsieh. 2015. "Performance test and analysis after high pressure turbine retrofit for Maanshan nuclear power plant Unit 1." Journal of the Chinese Institute of Engineers 38, no. 7: 918-927.

Articles
Published: 01 July 2013 in Journal of the Chinese Institute of Engineers
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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.

ACS Style

Yea-Kuang Chan; Jyh-Cherng Gu. Developing a turbine cycle model using adaptive neuro-fuzzy inference system for Kuosheng nuclear power plant. Journal of the Chinese Institute of Engineers 2013, 36, 577 -588.

AMA Style

Yea-Kuang Chan, Jyh-Cherng Gu. Developing a turbine cycle model using adaptive neuro-fuzzy inference system for Kuosheng nuclear power plant. Journal of the Chinese Institute of Engineers. 2013; 36 (5):577-588.

Chicago/Turabian Style

Yea-Kuang Chan; Jyh-Cherng Gu. 2013. "Developing a turbine cycle model using adaptive neuro-fuzzy inference system for Kuosheng nuclear power plant." Journal of the Chinese Institute of Engineers 36, no. 5: 577-588.

Journal article
Published: 19 January 2012 in Energies
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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.

ACS Style

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 2012, 5, 101 -118.

AMA Style

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. 2012; 5 (1):101-118.

Chicago/Turabian Style

Yea-Kuang Chan; Jyh-Cherng Gu. 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.

Journal article
Published: 11 February 2009 in Journal of Electronic Packaging
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This study presents an approximation for determining an optimized thickness of a concentric heated rectangular plate and derives an analytical solution for spreading resistance of a spreader having orthotropic conductivities. The solution for the orthotropic plate is obtained by separation of variables, and the optimized thickness is determined by taking the derivative of the thermal resistance with respect to the spreader thickness. According to the calculated results, an enhanced in-plane spreading effect can reduce the spreading resistance. The spreading resistance dominates the overall resistance of thin plates, whereas the one-dimensional conduction resistance becomes important for thick plates. However, the predicted optimized thickness from the approximation shows a disparity from the analytical results, while the aspect ratio between a spreader and heat source is less than 0.2. Even so, the thermal resistance corresponding to the predicted thickness is still in good agreement with the analytical solution. The proposed approximation will be useful for practical thermal design of heat sinks by predetermining the spreader thickness.

ACS Style

Yen-Shu Chen; Kuo-Hsiang Chien; Yung-Shin Tseng; Yea-Kuang Chan. Determination of Optimized Rectangular Spreader Thickness for Lower Thermal Spreading Resistance. Journal of Electronic Packaging 2009, 131, 011004 .

AMA Style

Yen-Shu Chen, Kuo-Hsiang Chien, Yung-Shin Tseng, Yea-Kuang Chan. Determination of Optimized Rectangular Spreader Thickness for Lower Thermal Spreading Resistance. Journal of Electronic Packaging. 2009; 131 (1):011004.

Chicago/Turabian Style

Yen-Shu Chen; Kuo-Hsiang Chien; Yung-Shin Tseng; Yea-Kuang Chan. 2009. "Determination of Optimized Rectangular Spreader Thickness for Lower Thermal Spreading Resistance." Journal of Electronic Packaging 131, no. 1: 011004.