• KEPCO Journal on Electric Power and Energy
• /
• v.7 no.1
• /
• pp.15-20
• /
• 2021

• The Transactions of the Korean Institute of Power Electronics
• /
• v.7 no.2
• /
• pp.158-164
• /
• 2002

This paper presents a proper parallel control method using a conventional control and a repetitive control for improving the output voltage waveform of uninterruptable power supply. Although first-order prediction control method shows a good characteristics to rectifier load, it is not sufficient to reduce steady state errors generated in nonlinear loads such as rectifier loads and phase controled loads. So we also employed a repetitive control method. A repetitive control method can eliminate steady state errors in the distorted output voltage caused by cyclic loads. The presented control scheme is verified through simulation and experiment. Experimental results Implemented on a single phase PWM inverter equipped with a LC output filter with 3 kVA, 60 Hz are shown.

• Current Photovoltaic Research
• /
• v.4 no.2
• /
• pp.80-86
• /
• 2016

Since the last five years it has become a lot of solar power plants installed. However, by installing the large-scale solar power station it is not easy to predict the actual generation years. Because there are a variety of factors, such as changes daily solar radiation, temperature and humidity. If the power output can be measured accurately it predicts profits also we can measure efficiency for solar power plants precisely. Therefore, Prediction of power generation is forecast to be a useful research field. In this study, out discovering the factors that can improve the accuracy of the prediction of the photovoltaic power generation presents the means to apply them to the power generation amount prediction.

• KEPCO Journal on Electric Power and Energy
• /
• v.1 no.1
• /
• pp.163-168
• /
• 2015

In order to integrate high wind generation resources into power grid, it is an essential to predict power outputs of wind generating resources. As wind farm outputs depend on natural wind resources that vary over space and time, spatial modeling based on geographic information such as latitude and longitude is needed to estimate power outputs of wind generation resources. In this paper, we introduce the basic concept of spatial modeling and present the spatial prediction model based on Kriging techniques. The empirical data, wind farm power output in Texas, is considered to verify the proposed prediction model.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.62 no.3
• /
• pp.371-375
• /
• 2013

A new model about output power prediction of PV module with various tilted angles and cell to cell distances has been proposed in this paper. Light intensity arrived on a solar cell could be changed by characteristics of PV module materials. Refractive indices, thickness and absorption coefficients of glass, EVA, solar cell and Backsheet are used to predict output. Also, the incident angle of light is changed 0 to 90[$^{\circ}$] and cell to cell distances are 5, 10 15[mm]. Two types of light incident on a solar cell are considered which are direct to a solar cell and reflected from Backsheet. The intensity of the incident light directly into the solar cell is reduced through glass and EVA about 17.5[%] in theoretical way. It has an error of 2.26[%] compared with experimental result. The results for compare theoretical with experimental data is validated within the error of 6.3[%]. This paper would be a research material to predict output power when the PV module is installed outdoor or a building.

• Industrial Engineering and Management Systems
• /
• v.15 no.2
• /
• pp.148-155
• /
• 2016

Existing power plants may consume significant amounts of fuel and require high operating costs, partly because of poor electrical power output estimates. This paper suggests a continuous conditional random field (C-CRF) model to predict more precisely the full-load electrical power output of a base load operated combined cycle power plant. We introduce three feature functions to model association potential and one feature function to model interaction potential. Together, these functions compose the C-CRF model, and the model is transformed into a multivariate Gaussian distribution with which the operation parameters can be modeled more efficiently. The performance of our model in estimating power output was evaluated by means of a real dataset and our model outperformed existing methods. Moreover, our model can be used to estimate confidence intervals of the predicted output and calculate several probabilities.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.1
• /
• pp.211-218
• /
• 2012

Fossil fuel is used all over the world and it produces greenhouse gases due to fossil fuel use. Therefore, it cause global warming and is serious environmental pollution. In order to decrease the environmental pollution, we should use renewable energy which is clean energy. Among several renewable energy, wind energy is the most promising one. Wind power generation is does not produce environmental pollution and could not be exhausted. However, due to wind power generation has irregular power output, it is important to predict generated electrical energy accurately for smoothing wind energy supply. There, we consider use ramp characteristic to forecast accurate wind power output. The ramp increase and decrease rapidly wind power generation during in a short time. Therefore, it can cause problem of unbalanced power supply and demand and get damaged wind turbine. In this paper, we make prediction models using power ramp rate as well as wind speed and wind direction to increase prediction accuracy. Prediction model construction algorithm used multilayer neural network. We built four prediction models with PRR, wind speed, and wind direction and then evaluated performance of prediction models. The predicted values, which is prediction model with all of attribute, is nearly to the observed values. Therefore, if we use PRR attribute, we can increase prediction accuracy of wind power generation.

• Journal of IKEEE
• /
• v.23 no.1
• /
• pp.334-337
• /
• 2019

In response to the increasing demand for energy, research and development of next-generation energy is actively carried out around the world to replace fossil fuels. Among them, the specific gravity of solar power generation systems using infinity and pollution-free solar energy is increasing. However, solar power generation is so different from solar energy that it is difficult to provide stable power and the power production itself depends on the solar energy by region. To solve these problems in this paper, we have collected meteorological data such as actual regional solar irradiance, precipitation, temperature and humidity, and proposed a solar power output prediction system using logic-based fuzzy Neural Network.

• KEPCO Journal on Electric Power and Energy
• /
• v.2 no.4
• /
• pp.627-630
• /
• 2016

Recently, the size of wind farms is becoming larger, and the integration of high wind generation resources into power gird is becoming more important. Due to intermittency of wind generating resources, it is an essential to predict power outputs. In this paper, we introduce the basic concept of curvilinear regression, which is one of the method of wind power prediction. The empirical data, wind farm power output in Jeju Island, is considered to verify the proposed prediction model.

• Journal of Power Electronics
• /
• v.17 no.1
• /
• pp.1-10
• /
• 2017

This paper presents a novel predictive digital control method for boundary conduction mode PFC converters without the need for detecting the inductor current. In the proposed method, the inductor current is predicted by analytical equations instead of being detected by a sensing-resistor. The predicted zero-crossing point of the inductor current is determined by the values of the input voltage, output voltage and predicted inductor current. Importantly, the prediction of zero-crossing point is achieved in just a single switching cycle. Therefore, the errors in predictive calculation caused by parameter variations can be compensated. The prediction of the zero-crossing point with the proposed method has been shown to have good accuracy. The proposed method also shows high stability towards variations in both the inductance and output power. Experimental results demonstrate the effectiveness of the proposed predictive digital control method for PFC converters.