• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.295-298
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• 2005

A minimum commitment method (MCM) was applied to predict the creep rupture life of type 316LN SS. For this purpose, a number of the creep rupture data for the type 316LN SS were collected through literature survey and experimental data of KAERl, Using the short-term creep rupture data under 2000 hr, the long-term creep rupture life above $10^5$ hour was predicted by means of the MCM. An optimum value of A, P and G function, used in the MCM equation, was determined respectively, and the creep rupture life with the A values in different temperatures was compared with the experimental data and the predicted curves.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.2 s.257
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• pp.160-165
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• 2007

To predict long-term creep life from short-term creep life data, various parametric methods such as Larson-Mille. (L-M), Orr-Sherby-Dorn (O-S-D), Manson-Haferd (M-H) parameters, and a Minimum Commitment Method (MCM) were suggested. A number of the creep data were collected through literature surveys and experimental data produced in KAERI. The polynomial equations for type 316LN SS were obtained by the time-temperature parameters (TTP) and the MCM. Standard error (SE) and standard error of mean (SEM) values were obtained and compared with the each method for various temperatures. The TTP methods showed good creep-life prediction, but the MCM was much superior to the TTP ones at $700^{\circ}C$ and $750^{\circ}C$. It was found that the MCM were lower in the SE values when compared to the TTP methods.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.704-706
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• 1996

This paper presents a new algorithm of unit commitment for optimal operation in power system. The proposed method developed algorithm that determined generators considering load variations at each stages. It has established forecast unit commitment over time horizon at first and next calculated quality cost of generators and then committed generator that has minimum quality cost at unit commitment schedule over time horizon. It is used that Objet-Oriented Programming for effective realization, and simple handling of complex program. The proposed method has applied at example system and the results has shown superior economics and computational requirement than the conventional method.

• Korean Journal of Metals and Materials
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• v.46 no.3
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• pp.118-124
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• 2008

Abstract: A minimum commitment method(MCM) was applied to predict the long-term creep rupture life for type 316LN stainless steel(SS). Lots of the creep-rupture data for the type 316LN SS were collected through world-wide literature surveys and the experimental data of KAERI. Using these data, the long-term creep rupture life above ${10}^5$ hour was predicted by means of the MCM. In order to obtain the most appropriate value for the constant A being used in the MCM equation, trial and error method was used for the wide ranges from -0.12 to 0.12, and the best value was determined by using the coefficient of determination, $R^2$ which is a statistical parameter. A suitable value for the A in type 316LN stainless steel was found to be at -0.02 ~ -0.05 ranges. It is considered that the MCM will be superior in creep-life prediction to commonly-used timetemperature parametric method, because the P(T) and G($\sigma$) functions are determined from the regression method based on experimental data.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.137-140
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• 1996

This paper proposes a unit commitment scheduling method based on Parallel Genetic Algorithm(PGA). Due to a variety of constraints to be satisfied, such as the minimum up and down time constraints, the search space of the UC problem is highly nonconvex. So, we used transputer which is one of the practical parallel processors. It can give us fastness and effectiveness features of the proposed method for solving the problem. To show the effectiveness of the PGA based unit commitment scheduling, we tested results for system of 5 units and we can get desirable results.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.109-110
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• 2006

Various parametric methods, Larson-Miller (L-M), Orr-Sherby-Dorn (O-S-D), Manson-Haferd (M-H) parameters, and minimum commitment method (MCM), were used to predict longer rupture time from short-term creep data. A number of the creep data were collected through literature surveys and experimental data produced in KAERI for predicting the creep type of type 316LN SS. Polynomial equations for predicting the creep life were obtained by the time-temperature parameters (TTP) and the MCM. standard error (SE) and standard error or mean (SEM) values were compared for the each method with temperatures. The TTP methods were good in the creep-life prediction, but the MCM was much superior to the TTP ones at $700^{\circ}C\;and\;750^{\circ}C$. The MCM was found to be lower in the SE values compared to the TTP methods

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.717-725
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• 2011

In the power system with an electric storage system that can increase utilization rate of the source of such new renewable energy, this paper introduces the approach on the daily unit commitment scheduling that determines simultaneously optimum operational condition and output of thermal generators and electric storage device. The unit commitment is one of the most important issues in economic operation and security of short-term operational plan of the power system. It is to determine on/off status of generator to minimize operational cost during the given period. The committed generator should satisfy various operational limitation such as estimated demand by system, spinning reserve condition within minimum operational cost. In order to determine on/off or charge/discharge/idle condition and output level of units and electric storage system, the MILP(Mixed Integer Linear Programming) is suggested. The proposed approach is the mixed method between LP(Linear Programming) and IP(integer programming) which seeks the value of real number and integer that maximize or minimize function objective within given condition. The daily unit commitment problem with the electric storage system is applied to MILP algorithm through linearization and formulation process. The proposed approach is applied to the test system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.11a
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• pp.85-86
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• 2007

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.959-968
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• 2013

This paper presents a new approach with artificial immune system algorithm to solve the profit based unit commitment problem. The objective of this work is to find the optimal generation scheduling and to maximize the profit of generation companies (Gencos) when subjected to various constraints such as power balance, spinning reserve, minimum up/down time and ramp rate limits. The proposed hybrid method is developed through adaptive search which is inspired from artificial immune system and genetic algorithm to carry out profit maximization of generation companies. The effectiveness of the proposed approach has been tested for different Gencos consists of 3, 10 and 36 generating units and the results are compared with the existing methods.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.333-336
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• 1991

In this paper, Hopfield & Tank model-like artificial neural network structure is proposed, which can be used for the optimal path planning problems such as the unit commitment problems or the maintenance scheduling problems which have been solved by the dynamic programming method or the branch and bound method. To construct the structure of the neural network, an energy function is defined, of which the global minimum means the optimal path of the problem. To avoid falling into one of the local minima during the optimization process, the simulated annealing method is applied via making the slope of the sigmoid transfer functions steeper gradually while the process progresses. As a result, computer(IBM 386-AT 34MHz) simulations can finish the optimal unit commitment problem with 10 power units and 24 hour periods (1 hour factor) in 5 minites. Furthermore, if the full parallel neural network hardware is contructed, the optimization time will be reduced remarkably.