• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.953-957
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• 1994

A performance prediction method is presented in this paper for design of a rotating machinery having power split/circulated transmisson with slip elements and planetary gears. And internal power flow patterns of such systems are theoretically analyzed by using mathematical modeling. To estimate usefulness of the designed machinary, geometrical approach has been adopted through the performance locus diagram which represents overall characteristics of the system. This gives us complect prediction of the qualitative performane and effects of design factors such as system layout, types and gear ratios of planetary gears and disign parameters of slip elements. The results for one of them are compared with experimental ones using dynamometer for verification.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1213-1216
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• 1998

This paper presents the result of a feasibility study for the prediction method of fault symptoms on 22.9kV distribution line. In this paper, real distribution data was collected and analyzed to isolate failure signatures or parameters which were distinct behaviors before and after failure incident. A new strategy of analysis-based (event-date concept) prediction algorithm for the distribution insulators and a developed model system were also discussed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.554-558
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• 2004

In this paper, we introduce noise prediction program of HVAC system to assist low-noisy design of ship's cabin. The developed program calculates sound power levels at HVAC components considering primary and secondary noise generated by fan and duct element, duct element noise attenuation, and duct break-in noise based on the authentic empirical method suggested by NEBB and acoustic power balancing method. Sound pressure level at cabin with or without ceiling system is evaluated by the diffuse-field theory considering diffuser and duct break-out sound powers. Moreover, the program provides intuitive pre- and post-processors using modem GUI functions to help efficient modeling and evaluation of cabin and HVAC component noise. To validate the accuracy and convenience of the program, noise prediction for a HVAC system is demonstrated.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.825-832
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• 2022

In this paper, we discuss the design and implementation of predictive and diagnostic models for realizing intelligent predictive models by collecting and storing the power output of agricultural photovoltaic power generation systems. Our model predicts the amount of photovoltaic power generation using RNN, LSTM, and GRU models, which are recurrent neural network techniques specialized for time series data, and compares and analyzes each model with different hyperparameters, and evaluates the performance. As a result, the MSE and RMSE indicators of all three models were very close to 0, and the R2 indicator showed performance close to 1. Through this, it can be seen that the proposed prediction model is a suitable model for predicting the amount of photovoltaic power generation, and using this prediction, it was shown that it can be utilized as an intelligent and efficient O&M function in an agricultural photovoltaic system.

• 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.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.969-980
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• 2017

Galloping is one of the most serious vibration problems in transmission lines. Power lines can be extensively damaged owing to aerodynamic instabilities caused by ice accretion. In this study, the accident probability induced by galloping phenomenon was analyzed using logistic regression analysis. As former studies have generally concluded, main factors considered were local weather factors and physical factors of power delivery systems. Since the number of transmission towers outnumbers the number of weather observatories, interpolation of weather factors, Kriging to be more specific, has been conducted in prior to forming galloping accident estimation model. Physical factors have been provided by Korea Electric Power Corporation, however because of the large number of explanatory variables, variable selection has been conducted, leaving total 11 variables. Before forming estimation model, with 84 provided galloping cases, 840 non-galloped cases were chosen out of 13 billion cases. Prediction model for accidents by galloping has been formed with logistic regression model and validated with 4-fold validation method, corresponding AUC value of ROC curve has been used to assess the discrimination level of estimation models. As the result, logistic regression analysis effectively discriminated the power lines that experienced galloping accidents from those that did not.

• 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 Advanced Marine Engineering and Technology
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• v.30 no.6
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• pp.669-676
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• 2006

The optimum design and the performance analysis software called POSEIDON for the HAWT (Horizontal Axis Wind Turbine) is developed by use of BEMT, which is the standard computational technique for prediction of power curves of wind turbines. The Prandtl's tip loss theory is adopted to consider the blade tip loss. The lift and the drag coefficient of S-809 airfoil are predicted via X-FOIL and the post stall characteristics of S-809 also are estimated by the Viterna's equations.$^{[13]}$ All the predicted aerodynamic characteristics are fairly well agreed with the wind tunnel test results. performed by Sommers in Delft university of technology. The rated power of the testing rotor is 20kW(FIL-20) at design conditions. The experimental aerodynamic parameters and the X-FOIL data are used for the power Prediction of the FIL-20 respectively The comparison results shows good agreement in power prediction.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.10
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• pp.1423-1427
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• 2014

Quantity of the solar power generation is heavily influenced by weather. In other words, due to difference in insolation, different quantity may be generated. However, it does not mean all areas with identical insolation produces same quantity because of various environmental aspects. Additionally, geographic factors such as altitude, height of plant may have an impact on the quantity. Hence, through this research, we designed a system to predict efficiency of the solar power generation system by applying insolation, weather factor such as duration of sunshine, cloudiness parameter and location. By applying insolation, weather data that are collected from various places, we established a system that fits with our nation. Apart from, we produced a geographic model equation through utilizing generated data installed nationwide. To design a prediction model that integrates two factors, we apply fuzzy algorithm, and validate the performance of system by establishing simulation system.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.709-719
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• 2022

Recently, as the importance of environmental protection has emerged, interest in new and renewable energy is also increasing worldwide. In particular, the solar energy sector accounts for the highest production rate among new and renewable energy in Korea due to its infinite resources, easy installation and maintenance, and eco-friendly characteristics such as low noise emission levels and less pollutants during power generation. However, although climate prediction is essential since solar power is affected by weather and climate change, solar radiation, which is closely related to solar power, is not currently forecasted by the Korea Meteorological Administration. Solar radiation prediction can be the basis for establishing a reasonable new and renewable energy operation plan, and it is very important because it can be used not only in solar power but also in other fields such as power consumption prediction. Therefore, this study was conducted for the purpose of improving the accuracy of solar radiation. Solar radiation was predicted by a total of three weather variables, temperature, humidity, and cloudiness, and solar radiation outside the atmosphere, and the results were compared using various models. The CatBoost model was best obtained by fitting and comparing the Boosting series (XGB, CatBoost) and RNN series (Simple RNN, LSTM, GRU) models. In addition, the results were further improved through Time series cross-validation.