• Nuclear Engineering and Technology
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• v.33 no.1
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• pp.102-110
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• 2001

In this work, the model predictive control method was applied to a linear model and a nonlinear model of steam generators. The parameters of a linear model for steam generators are very different according to the power levels. The model predictive controller was designed for the linear steam generator model at a fixed power level. The proposed controller at the fixed power level showed good performance for any other power levels by designed changing only the input-weighting factor. As the input-weighting factor usually increases, its relative stability does so. The steam generator has some nonlinear characteristics. Therefore, the proposed algorithm has been implemented for a nonlinear model of the nuclear steam generator to verify its real performance and also, showed good performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.3
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• pp.492-498
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• 2010

Since it is almost impossible to train the dispatchers with real power system, the dispatcher training simulator(DTS) is used for the training. Among various components of the DTS, the power system model(PSM) emulates the dynamic behavior of the power system to calculate the frequency and voltage. The frequency is calculated from various parameters such as mechanical power of power plants, load, inertia, and the damping of the power system. In the PSM, the power plants are modeled as differential equations, so the mechanical power of the power plants are calculated by the numerical methods. Conventionally, the fixed step-size algorithm has been used in the PSM, however it has some drawbacks. This paper develops the prototype PSM using the Matlab, and analyzes the problems of the fixed step-size algorithm by comparing the results with those of PSCAD simulation. In order to overcome the limitations, this paper proposes a modified frequency calculation method using the adaptive step-size algorithm. From the simulation using the proposed method, it is verified that the accuracy of frequency calculation increases substantially while the simulation time is not greatly increased.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.698-704
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• 2012

This paper proposes a new methodology for evaluating the probabilistic reliability based grid expansion planning of composite power system including the Wind Turbine Generators. The proposed model includes capacity limitations and uncertainties of the generators and transmission lines. It proposes to handle the uncertainties of system elements (generators, lines, transformers and wind resources of WTG, etc.) by a Composite power system Equivalent Load Duration Curve (CMELDC)-based model considering wind turbine generators (WTG). The model is derived from a nodal equivalent load duration curve based on an effective nodal load model including WTGs. Several scenarios are used to choose the optimal solution among various scenarios featuring new candidate lines. The characteristics and effectiveness of this simulation model are illustrated by case study using Jeju power system in South Korea.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.289-291
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• 2000

In a competitive electricity power market, the price of electricity changes instantly, that of conventional market is predetermined and hardly changes. In such a new environment, customers' behaviors change instantly according to the changing electricity prices. If we develop a electricity load model that well describes the behavior of electricity consumers, we can utilize that model in forecasting the amount of future load, solving the load flow problem and finding the weak point of the system. In this paper new electricity model that considers the price of electricity and power factor of the load is presented. While conventional load model, which is demand function of electricity, uses the price of real and reactive power as the independent variable of the demand function. this new load model uses price of real power and penalty factor according to the power factor for the calculation of amount of electricity demand.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1164-1166
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• 1999

This paper presents Static Synchronous Series Compensator(SSSC) model for power flow study using IPLAN. In the proposed model, SSSC is represented by the equivalent load variation. The equivalent load consists of active power load specified by user and reactive power load which is founded for considering characteristic of SSSC. And this is implemented by IPLAN which is a macro-external program for PSS/E. Using this model, SSSC can be solved in load-flow by just calling the model in PSS/E. The proposed model was applied to a realistic power system for validity test.

• Journal of the Korean Solar Energy Society
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• v.39 no.6
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• pp.113-125
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• 2019

With the opening of the small power brokerage business market in December 2018, the small power trading market has started in Korea. Operators must submit the day-ahead estimates of power output and receive incentives based on its accuracy. Therefore, the accuracy of power generation forecasts is directly affects profits of the operators. The forecasting process for power generation can be divided into two procedure. The first is to forecast solar irradiation and the second is to transform forecasted solar irradiation into power generation. There are two methods for transformation. One is to simulate with physical model, and another is to use regression model. In this study, we found the best-fit regression model by analyzing hourly data of PV output and solar irradiation data during three years for 242 PV plants in Korea. The best model was not a linear model, but a sigmoidal model and specifically a Gompertz model. The combined linear regression and Gompertz curve was proposed because a the curve has non-zero y-intercept. As the result, R² and RMSE between observed data and the curve was significantly reduced.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2377-2386
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• 2021

To model the atmospheric dispersion of radionuclides released from nuclear accident is very important for nuclear emergency. But the uncertainty of model parameters, such as source term and meteorological data, may significantly affect the prediction accuracy. Data assimilation (DA) is usually used to improve the model prediction with the measurements. The paper proposed a parameter bias transformation method combined with Lagrangian puff model to perform DA. The method uses the transformation of coordinates to approximate the effect of parameters bias. The uncertainty of four model parameters is considered in the paper: release rate, wind speed, wind direction and plume height. And particle swarm optimization is used for searching the optimal parameters. Twin experiment and Kincaid experiment are used to evaluate the performance of the proposed method. The results show that the proposed method can effectively increase the reliability of model prediction and estimate the parameters. It has the advantage of clear concept and simple calculation. It will be useful for improving the result of atmospheric dispersion model at the early stage of nuclear emergency.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.918-922
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• 2016

As the number of wind farms operating has increased, the interest of the central unit commitment and dispatch for wind power has increased as well. Wind power forecast is necessary for effective power system management and operation with high wind power penetrations. This paper presents the centralized wind power forecasting method, which is a forecast to combine all wind farms in the area into one, using time series models. Also, this paper proposes a prediction model modified with wind forecast error compensation. To demonstrate the improvement of wind power forecasting accuracy, the proposed method is compared with persistence model and new reference model which are commonly used as reference in wind power forecasting using Jeju Island data. The results of case studies are presented to show the effectiveness of the proposed wind power forecasting method.

• Journal of Power Electronics
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• v.13 no.6
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• pp.1080-1089
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• 2013

In this paper, the superposition principle of a heat sink temperature rise is verified based on the mathematical model of a plate-fin heat sink with two mounted heat sources. According to this, the distributed coupling thermal impedance matrix for a heat sink with multiple devices is present, and the equations for calculating the device transient junction temperatures are given. Then methods to extract the heat sink thermal impedance matrix and to measure the Epoxy Molding Compound (EMC) surface temperature of the power Metal Oxide Semiconductor Field Effect Transistor (MOSFET) instead of the junction temperature or device case temperature are proposed. The new thermal impedance model for the power converters in Switched Reluctance Motor (SRM) drivers is implemented in MATLAB/Simulink. The obtained simulation results are validated with experimental results. Compared with the Finite Element Method (FEM) thermal model and the traditional thermal impedance model, the proposed thermal model can provide a high simulation speed with a high accuracy. Finally, the temperature rise distributions of a power converter with two control strategies, the maximum junction temperature rise, the transient temperature rise characteristics, and the thermal coupling effect are discussed.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.869-877
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• 2016

Necessity has compelled man to improve upon the art of tapping wind energy for power generation; an apt reliever of strain exerted on the non-renewable fossil fuel. The power generation in a Wind Farm (WF) depends on site and wind velocity which varies with time and season which in turn determine wind power modeling. It implies, the development of an accurate wind speed model to predict wind power fluctuations at a particular site is significant. In this paper, Box-Jenkins ARIMA (Auto Regressive Integrated Moving Average) time series model for wind speed is developed for a 99MW wind farm in the southern region of India. Because of the uncertainty in wind power developed, the economic viability and reliability of power generation is significant. Life Cycle Costing (LCC) method is used to determine the economic viability of WF generated power. Reliability models of WF are developed with the help of load curve of the utility grid and Capacity Outage Probability Table (COPT). ARIMA wind speed model is used for developing COPT. The values of annual reliability indices and variations of risk index of the WF with system peak load are calculated. Such reliability models of large WF can be used in generation system planning.