• Journal of Power Electronics
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• v.15 no.6
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• pp.1619-1627
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• 2015

This paper presents a power system harmonic elimination using the mixed adaptive linear neural network and variable step-size leaky least mean square (ADALINE-VSSLLMS) control algorithm based active power filter (APF). The weight vector of ADALINE along with the variable step-size parameter and leakage coefficient of the VSSLLMS algorithm are automatically adjusted to eliminate harmonics from the distorted load current. For all iteration, the VSSLLMS algorithm selects a new rate of convergence for searching and runs the computations. The adopted shunt-hybrid APF (SHAPF) consists of an APF and a series of 7^th tuned passive filter connected to each phase. The performance of the proposed ADALINE-VSSLLMS control algorithm employed for SHAPF is analyzed through a simulation in a MATLAB/Simulink environment. Experimental results of a real-time prototype validate the efficacy of the proposed control algorithm.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Journal of Power Electronics
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• v.16 no.4
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• pp.1426-1437
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• 2016

In different industrial and mission oriented applications, redundant or standby semiconductor systems can be implemented to improve the reliability of power electronics equipment. The proper structure for implementation can be one of the redundant or standby structures for series or parallel switches. This selection is determined according to the type and failure rate of the fault. In this paper, the reliability and the mean time to failure (MTTF) for each of the series and parallel configurations in two redundant and standby structures of semiconductor switches have been studied based on different failure rates. The Markov model is used for reliability and MTTF equation acquisitions. According to the different values for the reliability of the series and parallel structures during SC and OC faults, a comprehensive comparison between each of the series and parallel structures for different failure rates will be made. According to the type of fault and the structure of the switches, the reliability of the switches in the redundant structure is higher than that in the other structures. Furthermore, the performance of the proposed series and parallel structures of switches during SC and OC faults, results in an improvement in the reliability of the boost dc/dc converter. These studies aid in choosing a configuration to improve the reliability of power electronics equipment depending on the specifications of the implemented devices.

• New & Renewable Energy
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• v.10 no.2
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• pp.19-28
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• 2014

Wind power generation of 5 MW wind turbine was predicted by using wind measurement data from HeMOSU-1 which is at south west coast of Korea. Time histories of turbulent wind was generated from 10-min mean wind speed and then they were used as input to Bladed to estimated electric power. Those estimated powers are used in both polynominal regression and neural network training. They were compared with each other for daily production and yearly production. Effect of mean wind speed and turbulence intensity were quantitatively analyzed and discussed. This technique further can be used to assess lifetime power of wind turbine.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.723-734
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• 2021

Wind energy is one of the rapidly developing renewable energies which is being developed and invested in response to climate change. As renewable energy policies and power plant installations are promoted, the supply of wind power in Korea is gradually expanding and attempts to accurately predict demand are expanding. In this paper, the ARIMA and ARIMAX models which are Time series techniques and the SVR, Random Forest and XGBoost models which are machine learning models were compared and analyzed to predict wind power generation in the Jeonnam and Gyeongbuk regions. Mean absolute error (MAE) and mean absolute percentage error (MAPE) were used as indicators to compare the predicted results of the model. After subtracting the hourly raw data from January 1, 2018 to October 24, 2020, the model was trained to predict wind power generation for 168 hours from October 25, 2020 to October 31, 2020. As a result of comparing the predictive power of the models, the Random Forest and XGBoost models showed the best performance in the order of Jeonnam and Gyeongbuk. In future research, we will try not only machine learning models but also forecasting wind power generation based on data mining techniques that have been actively researched recently.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.785-799
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• 2018

With the development of renewable energy sector, the importance of solar energy is continuously increasing. Solar radiation forecasting is essential to accurately solar power generation forecasting. In this paper, we used time series models (ARIMA, ARIMAX, seasonal ARIMA, seasonal ARIMAX, ARIMA GARCH, ARIMAX-GARCH, seasonal ARIMA-GARCH, seasonal ARIMAX-GARCH). We compared the performance of the models using mean absolute error and root mean square error. According to the performance of the models without exogenous variables, the Seasonal ARIMA-GARCH model showed better performance model considering the problem of heteroscedasticity. However, when the exogenous variables were considered, the ARIMAX model showed the best forecasting accuracy.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.4
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• pp.182-187
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• 2017

Synchronous generators have been significantly applied in large-scale power plants and its monitoring systems are additionally established to sequentially observe states and outputs. We develop a computer based monitoring device for three-phase synchronous power generators in this paper. First, a test-bed of such generator system is created and then a interface board is constructed to transfer electric signals including the output voltage and the current from generators into a computer system via a data acquisition device. Its RMS(root-mean-square) values are continuously shown on a screen of computer systems and its time-histories graphs are additionally illustrated under a graphic user interface(GUI) mode. Lastly, we carry out real-time experiments using the generator system with the monitoring device to demonstrate its reliability and superiority by comparing results of a generic power analyzer which is well-used in measuring various power systems practically.

• Proceedings of the Korean Reliability Society Conference
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• 2006.05a
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• pp.75-75
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• 2006

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.161-167
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• 2019

Recently, demand forecasting techniques have been actively studied due to interest in stable power supply with surging power demand, and increase in spread of smart meters that enable real-time power measurement. In this study, we proceeded the deep learning prediction model experiments which learns actual measured power usage data of home and outputs the forecasting result. And we proceeded pre-processing with moving average method. The predicted value made by the model is evaluated with the actual measured data. Through this forecasting, it is possible to lower the power supply reserve ratio and reduce the waste of the unused power. In this paper, we conducted experiments on three types of networks: Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) and we evaluate the results of each scheme. Evaluation is conducted with following method: MSE(Mean Squared Error) method and MAE(Mean Absolute Error).

• International Journal of Automotive Technology
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• v.2 no.1
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• pp.9-16
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• 2001

The lifetime of power train components can be improved dramatically by finding crack initiation points with suitable software tools and optimization of the critical areas. With increasing capacities of computers the prediction of the lifetime for components by numerical methods gets more and more important. This paper discusses some applications of the outstanding fatigue simulation program FEMFAT supporting the assessment of uniaxially and multiaxially loaded components (as well as welding seams and spot joints). The theory applied in FEMFAT differs in some aspects from classical approaches like the nominal stress concept or the local one and can be characterized by the term "influence parameter method". The specimen S/N-curve is locally modified by different influence parameters as stress-gradient to take into account notch effects, mean-stress influence which is quantified by means of a Haigh-diagram, surface roughness and treatments, temperature, technological size, etc. It is possible to consider plastic deformations resulting in mean-stress rearrangements. The dynamic loading of power train components is very often multiaxial, e.g. the stress state at each time is not proportional to one single stress state. Hence, the directions of the principal axes vary with time. We will present the way how such complex load situations can be handled with FEMFAT by the examples of a crank case and a gear box.