• Nuclear Engineering and Technology
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• v.21 no.4
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• pp.294-301
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• 1989

The Axial Power Distribution Monitoring System(APDMS) program was developed to calculate a detailed axial power distribution using two-level excore detector, cold leg temperature and control rod position signals. The unnormalized two-level excore detector signals were corrected for the rod shadowing factor determined by control rod position and for the temperature shadowing factor calculated based on cold leg temperature. A shape annealing matrix was then applied to the corrected excore detector response to yield peripheral power. After the core average power was obtained using linear relationship bet-ween core average and peripheral power, the boundary point power correction coefficient was applied to core average power in order to obtain boundary power for both upper and lower core axial boundaries. Then, the axial power distribution was synthesized by spline approximation. In spite of burnup, power level, control rod postion and axial offset changes, the comparisons of axial power distributions between BOXER simulation program and APDMS results showed good agreements within 5% root mean square error for Kori Unit 3 Cycle 4.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.11
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• pp.68-76
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• 2014

This paper presents an offset error compensation algorithm for the accurate phase angle of the grid voltage in single-phase grid-connected inverters. The offset error generated from the grid voltage measurement process cause the fundamental harmonic component with grid frequency in the synchronous reference frame phase lock loop (PLL). As a result, the grid angle is distorted and the power quality in power systems is degraded. In addition, the dq-axis currents in the synchronous reference frame and phase current have the dc component, first and second order ripples compared with the grid frequency under the distorted grid angle. In this paper, the effects of the offset and scaling errors are analyzed based on the synchronous reference frame PLL. Particularly, the offset error can be estimated from the integrator output of the synchronous reference frame PLL and compensated by using proportional-integral controller. Moreover, the RMS (Root Mean Square) function is proposed to detect the offset error component. The effectiveness of the proposed algorithm is verified through simulation and experiment results.

• Wind and Structures
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• v.31 no.5
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• pp.419-430
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• 2020

Weibull distribution is a conspicuous distribution known for its accuracy and its usage for wind energy analysis. The two and three parameter Weibull distributions are adopted in this study to fit wind speed data. The daily mean wind speed data of Ennore, Tamil Nadu, India has been used to validate the procedure. The parameters are estimated using maximum likelihood method, least square method and moment method. Four statistical tests namely Root mean square error, R2 test, Kolmogorov-Smirnov test and Anderson-Darling test are employed to inspect the fitness of Weibull probability density functions. The value of shape factor, scale factor, wind speed and wind power are determined at a height of 100m using extrapolation of numerical equations. Also, the value of capacity factor is calculated mathematically. This study provides a way to evaluate feasible locations for wind energy assessment, which can be used at any windy site throughout the world.

• Ocean Systems Engineering
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• v.8 no.4
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• pp.361-380
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• 2018

The dynamic performance of the wave energy converter (WEC) rotor with different geometric parameters such as depth of submergence and beak angle has been assessed by considering the linear potential flow theory using WAMIT solver and along with the computational fluid dynamics (CFD). The effect of viscous damping is incorporated by conducting numerical free decay test using CFD. The hydrodynamic coefficients obtained from the WAMIT, viscous damping from the CFD and estimated PTO damping are used to solve the equation of motion to obtain the final pitch response, mean optimal power and capture width. The viscous damping is almost 0.9 to 4.6 times when compared to the actual damping. It is observed that by neglecting the viscous damping the pitch response and power are overestimated when compared to the without viscous damping. The performance of the pitch WEC rotor in the Jeju western coast at the Chagwido is analyzed using Joint North Sea Wave Project (JONSWAP) spectrum and square-root of average extracted power is obtained. The performance of WEC rotor with depth of submergence 2.8 m and beak angle $60^{\circ}$ found to be good compared to the other rotors.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.101-106
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• 2014

This paper presents a wide dynamic range radio-frequency (RF) root-mean-square (RMS) power detector using an automatic gain control (AGC) loop. The AGC loop consists of a variable gain amplifier (VGA), RMS conversion block and gain control block. The VGA exploits dB-linear gain characteristic of the cascade VGA. The proposed circuit utilizes full-wave squaring and generates a DC voltage proportional to the RMS of an input RF signal. The proposed RMS power detector operates from 500MHz to 5GHz. The detecting input signal range is from 0 dBm to -70 dBm or more with a conversion gain of -4.53 mV/dBm. The proposed RMS power detector is designed in a 65-nm 1.2-V CMOS process, and dissipates a power of 5 mW. The total active area is $0.0097mm^2$.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.3
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• pp.29-41
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• 2010

In this study, the prediction errors of various spatial interpolation methods used to model values at unmeasured locations was compared and the accuracy of these predictions was evaluated. The root mean square (RMS) was calculated by processing different parameters associated with spatial interpolation by using techniques such as inverse distance weighting, kriging, local polynomial interpolation and radial basis function to known elevation data of the east coastal area under the same condition. As a result, a circular model of simple kriging reached the smallest RMS value. Prediction map using the multiquadric method of a radial basis function was coincident with the spatial distribution obtained by constructing a triangulated irregular network of the study area through the raster mathematics. In addition, better interpolation results can be obtained by setting the optimal power value provided under the selected condition.

• 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 the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.177-187
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• 2014

This paper proposes a fast subspace tracking methods, which is called GVFF FAPI, based on FAPI (Fast Approximated Power Iteration) method and GVFF RLS (Gradient-based Variable Forgetting Factor Recursive Lease Squares). Since the conventional FAPI uses a constant forgetting factor for estimating covariance matrix of source signals, it has difficulty in applying to non-stationary environments such as continuously changing DOAs of source signals. To overcome the drawback of conventioanl FAPI method, the GVFF FAPI uses the gradient-based variable forgetting factor derived from an improved means square error (MSE) analysis of RLS. In order to achieve the decreased subspace error in non-stationary environments, the GVFF-FAPI algorithm used an improved forgetting factor updating equation that can produce a fast decreasing forgetting factor when the gradient is positive and a slowly increasing forgetting factor when the gradient is negative. Our numerical simulations show that GVFF-FAPI algorithm offers lower subspace error and RMSE (Root Mean Square Error) of tracked DOAs of source signals than conventional FAPI based MUSIC (MUltiple SIgnal Classification).

• Wind and Structures
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• v.31 no.4
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• pp.311-319
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• 2020

India is a developing nation and heavily spends on the development of wind power plants to meet the national energy demand. The objective of this paper is to investigate wind power potential of Ennore site using wind data collected over a period of two years by three parameter Weibull distribution. The Weibull parameters are estimated using maximum likelihood, least square method and moment method and the accuracy is determined using R² and root mean square error values. The site specific capacity factor is calculated by the mathematical model developed by three parameter Weibull distribution at different hub heights above the ground level. At last, the wind energy economic analysis is carried out using capacity factor at 30 m, 40 m and 50 m height for different wind turbine models. The analysis showed that the site has potential to install utility wind turbines to generate energy at the lowest cost per kilowatt-hour at height of 50 m. This research provides information of wind characteristics of potential sites and helps in selecting suitable wind turbine.

• Journal of Environmental Impact Assessment
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• v.29 no.4
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• pp.272-285
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• 2020

We investigated statistical evaluation parameters for 3D meteorological and air quality models and selected several quantitative indicator references, and summarized the reference values of the statistical parameters for domestic air quality modeling researcher. The finally selected 9 statistical parameters are MB (Mean Bias), ME (Mean Error), MNB (Mean Normalized Bias Error), MNE (Mean Absolute Gross Error), RMSE (Root Mean Square Error), IOA (Index of Agreement), R (Correlation Coefficient), FE (Fractional Error), FB (Fractional Bias), and the associated reference values are summarized. The results showed that MB and ME have been widely used in evaluating the meteorological model output, and NMB and NME are most frequently used for air quality model results. In addition, discussed are the presentation diagrams such as Soccer Plot, Taylor diagram, and Q-Q (Quantile-Quantile) diagram. The current results from our study is expected to be effectively used as the statistical evaluation parameters suitable for situation in Korea considering various characteristics such as including the mountainous surface areas.