• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.903-906
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• 1997

When large capacity generation stations that consist of several large units tend to pull out of step from main power system, stabilizing control scheme as emergency control for preventing loss of synchronism of the whole stations with the remaining system is devided into two steps that the first step is to perform on-line prediction for out-of-step and the next step is on-line calculation of the amount of generation shedding for the rest of generators to be in step when out of step is expected. This paper presents on-line prediction scheme for out-of-step based on P-$\delta$ curve estimation using real-time measurement and on-line calculation of generation shedding. The proposed stabilizing scheme was applied to case study of real power system and the results obtained by the method compare well with the results by simulation.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.353-360
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• 2016

Recently, solar photovoltaic(PV) power generation which generates electrical power from solar panels composed of multiple solar cells, showed the most prominent growth in the renewable energy sector worldwide. However, in spite of increased demand and need for a photovoltaic power generation, it is difficult to early detect defects of solar panels and equipments due to wide and irregular distribution of power generation. In this paper, we choose an optimal machine learning algorithm for estimating the generation amount of solar power by considering several panel information and climate information and develop a defect detection system by using the chosen algorithm generation. Also we apply the algorithm to a domestic solar photovoltaic power plant as a case study.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.219-224
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• 2019

The number of solar power installation companies including domestic small scale (50kW or less) is increasing rapidly, but the efficient operation system and management are insufficient. Therefore, a new type of operating system is needed as a maintenance management aspect to maximize the generation amount in the current state rather than the additional function which causes the increase of the generation cost. In this paper, we utilize Big Data and sensor network to maximize the operating efficiency of solar power plant and analyze the expert system to develop power generation prediction technology, module unit fault detection technology, life prediction of inverter components and report technology, maintenance optimization And to develop a smart monitoring system that enables optimal operation of photovoltaic power plants such as development of algorithms and economic analysis.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1173-1178
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• 2011

The power generated by wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to predict the changing wind power. In this paper, neural network based wind power prediction scheme which uses wind speed and direction is considered. In order to get a better prediction result, compression function which can be applied to the measurement data is introduced. Empirical data obtained from wind farm located in Kunsan is considered to verify the performance of the compression function.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.4
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• pp.627-630
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• 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 IKEEE
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• v.26 no.3
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• pp.355-363
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• 2022

Korea is pursuing a plan to switch and expand energy sources with a focus on renewable energy with the goal of becoming carbon neutral by 2050. As the instability of energy supply increases due to the intermittent nature of renewable energy, accurate prediction of the amount of renewable energy generation is becoming more important. Therefore, the government has opened a small-scale power brokerage market and is implementing a system that pays settlements according to the accuracy of renewable energy prediction. In this paper, a prediction model was implemented using a statistical model and an artificial intelligence model for the prediction of solar power generation. In addition, the results of prediction accuracy were compared and analyzed, and the revenue from the settlement amount of the renewable energy generation forecasting system was estimated.

• Journal of Wind Energy
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• v.15 no.2
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• pp.10-22
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• 2024

This research is a comprehensive analysis of wind power prediction sensitivity using a Long Short-Term Memory (LSTM) deep learning neural network model, accounting for the inherent uncertainties in wind speed estimation. Utilizing a year's worth of operational data from an operational wind farm, the study forecasts the power output of both individual wind turbines and the farm collectively. Predictions were made daily at intervals of 10 minutes and 1 hour over a span of three months. The model's forecast accuracy was evaluated by comparing the root mean square error (RMSE), normalized RMSE (NRMSE), and correlation coefficients with actual power output data. Moreover, the research investigated how inaccuracies in wind speed inputs affect the power prediction sensitivity of the model. By simulating wind speed errors within a normal distribution range of 1% to 15%, the study analyzed their influence on the accuracy of power predictions. This investigation provided insights into the required wind speed prediction error rate to achieve an 8% power prediction error threshold, meeting the incentive standards for forecasting systems in renewable energy generation.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.64-71
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• 2017

Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.245-253
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• 2011

Due to the imprudent spending of the fossil fuels, the environment was contaminated seriously and the exhaustion problems of the fossil fuels loomed large. Therefore people become taking a great interest in alternative energy resources which can solve problems of fossil fuels. The wind power energy is one of the most interested energy in the new and renewable energy. However, the plants of wind power energy and the traditional power plants should be balanced between the power generation and the power consumption. Therefore, we need analysis and prediction to generate power efficiently using wind energy. In this paper, we have performed a research to predict power generation patterns using the wind power data. Prediction approaches of datamining area can be used for building a prediction model. The research steps are as follows: 1) we performed preprocessing to handle the missing values and anomalous data. And we extracted the characteristic vector data. 2) The representative patterns were found by the MIA(Mean Index Adequacy) measure and the SOM(Self-Organizing Feature Map) clustering approach using the normalized dataset. We assigned the class labels to each data. 3) We built a new predicting model about the wind power generation with classification approach. In this experiment, we built a forecasting model to predict wind power generation patterns using the decision tree.

• New & Renewable Energy
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• v.2 no.3
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• pp.15-22
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• 2006

As the distributed generation becomes more reliable and economically feasible, it is expected that a higher application of the distributed generation units would be interconnected to the existing grids. This new market penetration using the distributed generation technology is linked to a large number of factors like economics and performance, safety and reliability, market regulations, environmental issues, or grid connection standards. KEPCO, a government company in Korea, has performed the project to identify and evaluate the performance of Micro Gas Turbine(MGT) technologies focused on 30, 60kW-class grid-connected optimization and combined Heat & Power performance. This paper describes the results for the mechanical, electrical, and environmental tests of MGT on actual grid-connection under Korean regulations. As one of the achievements, the simulation model of Exhaust-gas Absorption Chiller was developed, so that it will be able to analyze or propose new distributed generation system using MGT. In addition, KEPCO carried out the field testing of the MGT Cogeneration system at the R&D Center Building, KEPCO. The field test was conducted in order to respond to a wide variety of needs for heat recovery and utilization. The suggested method and experience for the evaluation of the distributed generation will be used for the introduction of other distributed generation technologies into the grid in the future.