• Journal of the Korean Solar Energy Society
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• v.38 no.4
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• pp.43-54
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• 2018

In this study, we assessed dependency of small hydropower potentials on the two different runoff such as the estimated runoff based on the rainfall amounts and measured runoff. The hydpropower potentials were evaluated using actural power generations taken from Deoksong, Hanseok, and Socheon small hydropower plants over Han and Nakdong river basins, respectively. As a result of comparing the actual power generation amount with the potential amount based on the rainfall amount and the estimated amount based on the observed flow amount by each small hydroelectric power plant, the degree of latent small hydro energy by the observed flow was confirmed to be high. It is confirmed that the potential hydroelectric power generation rate is estimated to be about average 30%Point higher than the actual generation amount as a result of the measured flow rate rather than using the rainfall amount. Based on this, a method for improving the degree of the actual generation amount is proposed.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.211-218
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• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• Journal of the Korean Society for Railway
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• v.20 no.4
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• pp.474-481
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• 2017

When designing photovoltaic power plants in Korea, the prediction of photovoltaic power generation at the design phase is carried out using PVSyst, PVWatts (Overseas power generation prediction software), and overseas weather data even if the test site is a domestic site. In this paper, for a comparative study to predict power generation using weather information, domestic photovoltaic power plants in two regions were selected as target sites. PVsyst, which is a commercial power generation forecasting program, was used to compare the accuracy between the predicted value of power generation (obtained using overseas weather information (Meteonorm 7.1, NASA-SSE)) and the predicted value of power generation obtained by the Korea Meteorological Administration (KMA). In addition, we have studied ways to improve the prediction of power generation through comparative analysis of meteorological data. Finally, we proposed a revised solar power generation prediction model that considers climatic factors by considering the actual generation amount.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.237-242
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• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.23-30
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• 2022

Deep learning analyzes data to discover a series of rules and anticipates the future, helping us in various ways in our lives. For example, prediction of stock prices and agricultural prices. In this research, the results of solar photovoltaic power generation accompanied by weather are analyzed through deep learning in situations where the importance of solar energy use increases, and the amount of power generation is predicted. In this research, we propose a model using LSTM(Long Short Term Memory network) that stand out in time series data prediction. And we compare LSTM's performance with CNN(Convolutional Neural Network), which is used to analyze various dimensions of data, including images, and CNN-LSTM, which combines the two models. The performance of the three models was compared by calculating the MSE, RMSE, R-Squared with the actual value of the solar photovoltaic power generation performance and the predicted value. As a result, it was found that the performance of the LSTM model was the best. Therefor, this research proposes predicting solar photovoltaic power generation using LSTM.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.119-121
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• 2000

The possibility of a remote on-line determination of the maximum load rates that are basically set at generation plants is investigated in automatic generation control (AGC) system. Energy management system (EMS) generates a test input to a remote power plant to get the samples of the generator output. Then from the samples it can be attempted to determine an approximate value of the load rate limit set by the operator. It is shown in computer simulation that in actual power plants the limit can be approximately determined from the input-output characteristics of the plants for a unit-step input.

• Journal of Power Electronics
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• v.16 no.3
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• pp.1200-1208
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• 2016

Planar electromagnetic interference (EMI) filter has significant engineering significance to power electronic system integration and miniaturization. However, the value of differential mode capacitance cannot meet the demand of noise suppression because of the size limit of ceramics. In this case, the EMI filter of novel multilayers is recommended to address this issue. A novel integrated structure of EMI filter based on multilayer ceramic is proposed in this study. The inductance and capacitance of the new structure can be designed separately, which is an advantage in manufacturing. Insertion loss is measured more closely to the actual situation in this study, which is different from the condition where source and load impedances are both 50 Ω. In the process of designing a novel EMI filter, noise impedance is considered. Moreover, the prototype is created and applied to a small switching power supply, which verifies the effectiveness of the developed EMI filter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1495-1504
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• 2021

An ultra-compact binary power generation plant converts thermal energy into electric power using temperature difference between heat source and cooling source. In the actual power generation environment, the characteristic value of the plant changes due to any negative effects such as environmental condition or corrosion of related equipment. If the characteristic value of the plant changes, it may lead to unstable output of the turbine in a conventional PID control system with fixed PID parameters. A Neuro PID control system based on Neural Network adaptively to adjust the PID parameters according to the change in the characteristic value of the plant is proposed in this paper. Discrete-time transfer function models to represent the dynamic characteristics near the operating point of the investigated plant are deduced, and a design strategy of the proposed control system is described. The proposed Neuro PID control system is compared with the conventional PID control system, and its effectiveness is demonstrated through the simulation results.

• Journal of the Korean Institute of Navigation
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• v.18 no.2
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• pp.159-171
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• 1994

Hull roughness due to corrosions of outer hull and had applications on outer hull paints was analyzed theoretically. It's value which was gainable practically, was studied, and estimated power penalty formular correspon-ding to that value were reviewed. Local roughness penalty and roughness texture penalty that paint manahers in ship yard can easily were compared and studied by dotting actual ships in the issued curves. Losses and benifits of hull roughness & the specification choise of A/F paint which managers of ship maintenance were much interested in have been calculated through actual ships. The paper is illustrating that how much the specification choise and managing of A/F paint have effects on fuel consumption of ship in program. It is urgently required that recent developed antifouling paints of new A/F generation should be adopted to new ship building by big shipping companies in view of the environmental protection and the economical maintenance of ships.

• Journal of the Korean Solar Energy Society
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• v.30 no.6
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• pp.118-124
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• 2010

In order to develop commercial model of 10kW dish-Stirling solar thermal power system, modification for the exiting facility was taken for a year as a Leading Project in KIER. During the project, solar tracking system, control and monitoring system and high durability reflector were developed and long term operation were performed. The solar tracking system was tested for four months to investigate the degree of precision and adapted to the control system for an actual operation from October in 2009. The sun tracking accuracy of ${\pm}4$ mrad using modified control system was obtained and the system operated successfully during the experimental period. The monitoring system displays engine pressure, electric generation amounts, generator RPM, receiver temperatures, and etc. from Stirling engine and weather data of Direct Normal Irradiation, Horizontal Global Insolation, wind speed & direction, and atmosphere temperature from weather station. According to the operating results in a clear sky day, electric power of 6,890 W was generated at the DNI value of 850 W/$m^2$ and the averaged solar-to-electricity efficiency during a whole day reached to 18.99%. From the overall operating results, linear power generation trend could be observed with increasing DNI value. The solar-to-electricity efficiency achieved to 19% around the DNI value of 700 W/$m^2$ and increased to 20% when the DNI value goes up to 900 W/$m^2$.