Prof. HYUNG KEUN AHN
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Academic Career Length
33Years (1994-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
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Journal Article
WLWaithiru Kamuyu Charles Lawrence
Waithiru Kamuyu Charles LawrenceJLJong Rok Lim
Jong Rok LimCWChang Sub Won
Chang Sub WonHYUNG KEUN AHNRapid reduction in the price of photovoltaic (solar PV) cells and modules has resulted in a rapid increase in solar system deployments to an annual expected capacity of 200 GW by 2020. Achieving high PV cell and module efficiency is necessary for many solar manufacturers to break even. In addition, new innovative installation methods are emerging to complement the drive to lower $/W PV system price. The floating PV (FPV) solar market space has emerged as a method for utilizing the cool ambient environment of the FPV system near the water surface based on successful FPV module (FPVM) reliability studies that showed degradation rates below 0.5% p.a. with new encapsulation material. PV module temperature analysis is another critical area, governing the efficiency performance of solar cells and module. In this paper, data collected over five-minute intervals from a PV system over a year is analyzed. We use MATLAB to derived equation coefficients of predictable environmental variables to derive FPVM’s first module temperature operation models. When comparing the theoretical prediction to real field PV module operation temperature, the corresponding model errors range between 2% and 4% depending on number of equation coefficients incorporated. This study is useful in validation results of other studies that show FPV systems producing 10% more energy than other land based systems.
26 February 2019 in IEEE Journal of Photovoltaics
Journal Article
GKGyuGwang Kim
GyuGwang KimJCJin Ho Choi
Jin Ho ChoiSPSo Young Park
So Young ParkBBByeong Gwan Bhang
Byeong Gwan BhangWNWoo Jun Nam
Woo Jun NamHYUNG KEUN AHN +2
With increasing installations of photovoltaic (PV) systems, interest in power forecasting has also increased. Inaccurate forecasts would result in substantial economic losses and system reliability issues. The correlation between weather variables and PV power is critical to ensure the efficient use of energy in PV systems. A key step toward accurate power forecasting is estimating the output from a PV system based on known environmental input data. In this research, all available weather data are used to predict the PV power. Meteorological and power data are then analyzed using a statistical approach to identify the order of significance of the input variables. Then, a predictive model is suggested as a function of irradiance, ambient temperature, wind speed, and relative humidity. The model produces a root mean square error of 4.957% and a mean absolute percentage error of 5.468% during the measurement period and over the entire range of irradiation.
12 July 2004 in IEEE Transactions on Power Electronics
Journal Article
A thermal resistor-capacitor (RC) model is introduced for the power insulated gate bipolar transistor (IGBT) modules used in a three-phase inverter. The parameters of the model are extracted from the experimental data for the transient thermal impedance from-junction-to-case Z/sub jc/ and case-to-ambient Z/sub ca/. The accuracy of the RC model is verified by comparing its predictions with those resulting from the three-dimensional finite element method simulation. The parameter extraction algorithm is easy to adapt to other types of power modules in an industrial application environment.
Journal Article
HYUNG KEUN AHN
Neungsoo NeungsooPhotovoltaic (PV) power fluctuations caused by weather changes can lead to short-term mismatches in power demand and supply. Therefore, to operate the power grid efficiently and reliably, short-term PV power forecasts are required against these fluctuations. In this paper, we propose a deep RNN-based PV power short-term forecast. To reflect the impact of weather changes, the proposed model utilizes the on-site weather IoT dataset and power data, collected in real-time. We investigated various parameters of the proposed deep RNN-based forecast model and the combination of weather parameters to find an accurate prediction model. Experimental results showed that accuracies of 5 and 15 min ahead PV power generation forecast, using 3 RNN layers with 12 time-step, were 98.0% and 96.6% based on the normalized RMSE, respectively. Their R2-scores were 0.988 and 0.949. In experiments for 1 and 3 h ahead of PV power generation forecasts, their accuracies were 94.8% and 92.9%, respectively. Also, their R2-scores were 0.963 and 0.927. These experimental results showed that the proposed deep RNN-based short-term forecast algorithm achieved higher prediction accuracy.
29 July 2019 in IEEE Journal of Photovoltaics
Journal Article
BBByeong Gwan Bhang
Byeong Gwan BhangWLWonbin Lee
Wonbin Lee
GKGyuGwang Kim
GyuGwang KimJCJin Ho Choi
Jin Ho Choi
SPSo Young Park
So Young Park
HYUNG KEUN AHNUnlike conventional photovoltaic (PV) modules that generate power by absorbing light through the front side only, a bifacial PV module can generate power by absorbing light through the rear as well as the front, which would lead to an enhancement of power generation. Particularly, bifacial PV modules would have the advantage of lower power loss in shaded environments than monofacial PV modules, thanks to the light absorbed through the rear side. To predict the power of a bifacial PV module in a shaded environment, modeling is suggested by considering the shaded areas, the operational status of the bypass diodes, and the temperature of the bifacial PV module. To verify the power prediction of a bifacial PV module with a shaded area, modeled and measured powers are compared, showing error rates of 7.28%. From the results of the power loss experiments for bifacial and monofacial PV modules in shaded environments, it is confirmed that the bifacial PV module shows a relatively low power loss rate when compared with the monofacial PV module, with a power loss rate being 87.26% of the rate for the monofacial PV module.
1 June 1994 in IEEE Transactions on Electron Devices
Journal Article
HYUNG KEUN AHNMNM. El Nokali
M. El Nokali
About Me
Hyung-Keun Ahn received the B.S. and M.S. from Yonsei University, Seoul South Korea and the Ph.D. from the Electrical Engineering Department, University of Pittsburgh, Pittsburgh, PA, USA, in 1993. From 1986 to 1995, he was with the LG Semiconductor Co. working on silicon-based device design and process integration. In 1995, he joined the Department of Electrical Engineering, Konkuk University, Seoul Korea, where he is a professor and dean of Next Generation PV Module and Power System R&D Center. From 2014 to 2016, he was also a foreign professor in the department of electrical and control engineering, division of EE, SDUT (Shandong University of Technology), Zibo, Shandong, China. He is currently working on fault detection of PV station and microgrid network design for net-zero energy environments using renewable energy resources including ESS, wind and fuel cell with floating and marine photovoltaic systems under the demand response control for RE100. His research interests include reliability of both Si and GaAs-based solar cells and PV modules for applications in micro roof-top to large scale of PVs connected with ESS and his key focuses stay on failure analysis, reliability, and maintenance, repair, and operation for durable renewable energy system. Dr. Ahn was a Committee Member of the National Science and Technology Commission, Department of Energy, South Korea, from 2005 to 2007.