• New & Renewable Energy
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• v.8 no.3
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• pp.14-22
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• 2012

Performance of photovoltaic (PV) generator systems relies on its operating conditions. Maximum power extracted from PV generators depends strongly on solar irradiation, load impedance, and ambient temperature. A most maximum power point tracking (MPPT) algorithm is based on a perturb and observe method and an incremental conductance method. It is well known the latter is better in terms of dynamics and tracking characteristics under condition of rapidly changing solar irradiation. However, in case of digital implementation, the latter has some error for determining a maximum power point. This paper presents a PID based MPPT algorithm for such PV systems. We use neural network technique for determining PID parameters by online learning approach. And we construct a boost converter to regulate the output voltage from PV generator system. Computer simulation is carried out to evaluate the proposed MPPT method and we accomplish comparative study with a perturb and observe based MPPT method to prove its superiority.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.85-88
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• 2000

This paper describes a Band-Selective Charge-Pump PLL(CPPLL) for clock recovery and clock generator. The proposed PLL satisfies fast acquisition time and low jitter characteristics simultaneously by reducing initial frequency error. The acquisition time of the designed Band-Selective CPPLL can be decreased down to 55% of a conventional CPPLL.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.783-793
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• 2019

Purpose: The purpose of this study is to apply the machine and deep learning methodology on error terms which are continuously auto-generated on the sensors with specific time period and prove the improvement effects of power generator prediction diagnosis system by comparing detection ability. Methods: The SVM(Support Vector Machine) and MLP(Multi Layer Perception) learning procedures were applied for predicting the target values and sequentially producing the error terms for confirming the detection improvement effects of suggested application. For checking the effectiveness of suggested procedures, several detection methodologies such as Cusum and EWMA were used for the comparison. Results: The statistical analysis result shows that without noticing the sequential trivial changes on current diagnosis system, suggested approach based on the error term diagnosis is sensing the changes in the very early stages. Conclusion: Using pattern of error terms as a diagnosis tool for the safety control process with SVM and MLP learning procedure, unusual symptoms could be detected earlier than current prediction system. By combining the suggested error term management methodology with current process seems to be meaningful for sustainable safety condition by early detecting the symptoms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8753-8759
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• 2015

Establishment of analysis procedures and validated performance measurements for generator output is required to maintain stable management of generator output in turbine power generation cycle. We developed turbine expansion model and measurement validation model for the performance calculation of generator using turbine output based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). We also developed verification model for uncertain measurement data related to the turbine and generator output. Although the model in previous researches was developed using artificial neural network and kernel regression, the verification model in this paper was based on algorithms through Support Vector Machine (SVM) model to overcome the problems of unmeasured data. The selection procedures of related variables and data window for verification learning was also developed. The model reveals suitability in the estimation procss as the learning error was in the range of about 1%. The learning model can provide validated estimations for corrective performance analysis of turbine cycle output using the predictions of measurement data loss.

• Journal of Power Electronics
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• v.10 no.3
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• pp.313-319
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• 2010

This paper proposes a new compensation algorithm for the current measurement errors in a DFIG (Doubly Fed Induction Generator). Generally, current measurement path with current sensors and analog devices has non-ideal factors like offset and scaling errors. As a result, the dq-axis currents of the synchronous reference frame have one and two times ripple components of the slip frequency. In this paper, the main concept of the proposed algorithm is implemented by integrating the 3-phase rotor currents into the stationary reference frame to compensate for the measured current ripples in a DFIG. The proposed algorithm has several beneficial features: easy implementation, less computation time, and robustness with regard to variations in the electrical parameters. The effectiveness of the proposed algorithm is verified by several experiments.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.5
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• pp.383-392
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• 2007

In this paper, we study the prediction of depth and width of a defect in steam generator tube in nuclear power plant using neural network. To this end, we first generate eddy current testing (ECT) signals for 4 defect patterns of SG tube: I-In type, I-Out type, V-In type, and V-Out type. In particular, we generate 400 ECT signals for various widths and depths for each defect type by the numerical analysis program based on finite element modeling. From those generated ECT signals, we extract new feature vectors for the prediction of defect size, which include the angle between the two points where the maximum impedance and half the maximum impedance are achieved. Using the extracted feature vector, multi-layer perceptron with one hidden layer is used to predict the size of defects. Through the computer simulation study, it is shown that the proposed method achieves decent prediction performance in terms of maximum error and mean absolute percentage error (MAPE).

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.1
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• pp.27-35
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• 2009

In this paper, we study the classification of steam generator tube defects using an improved feature extraction. We consider 4 axisymmetric defect patterns of tube: I-In type, I-Out type, V-In type, and V-Out type. Through numerical analysis program based on finite element modeling, 400 ECT signals are generated by varying width and depth of each defect type. From those generated ECT signals, we propose new feature vectors that include an angle between the two points where the Maximum impedance and half the Maximum impedance, and angles between Maximum impedance point and 10%, 20%, 30%, 40% of Maximum impedance points. Also, multi-layer perceptron with one hidden layer is used to classify the defect patterns. Through the computer simulation study, it is shown that the proposed method achieves an improved defect classification performance in terms of Maximum Error and mean square Error.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.2
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• pp.44-50
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• 2002

Conventional acoustic echo cancelers using ES(Exponentially weighted Stepsize) algorithm have simple operational configuration and fast convergence speed batter then NLMS algorithm, but they are very weak in external noise because ES algorithm updates filter taps using an average energy reduction rate of room impulse response in specific acoustical condition. So, a new configuration of acoustic echo canceler with stepsize generator and selector is proposed in this thesis. The proposed stepsize generator and selector improve conventional acoustic echo canceler's weakness in external noise and improve the system robustness. The stepsize generator generates additional stepsize value using moving averager, which is the residual noise energy of error signal multiplied by constant ${\gamma}$. The stepsize selector selects the stepsize value that has better performance in an acoustic echo canceler using a coefficient decision factor ${\Delta}_{differ}$ The simulation results show that the proposed algorithm reduces residual error by 5[dB] to 10[dB], improves misadjustment regardless of external noise's SNR. 

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.12
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.9
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• pp.62-69
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• 1996

Interpolator is a very important element in NC machines in that it controls tool path and speed. In this paper, studied were extensive interpolation characteristics of reference pulse method among various interpolation and pulse generation methods. Specifically, processing speed and path error of DDA, SPD and SFG methods were compared and analyzed against line, circle and elipse. As a result, in the point of processing speed, SPD method was found to be the best for line interpolation, SFG method for circle and ellipse, and DDA method was found to be the slowest for all paths. In the point of path error, DDA method was found to have the biggest error for all kinds of paths.