• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.4
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• pp.566-574
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• 2013

In this paper, direct power control system for three-phase PFC AC/DC converter without the source voltage sensors is proposed. The sinusoidal input current and unity effective power factor are realised based on the estimated flux in the observer. Both active and reactive power calculated using estimated flux. The estimation of flux is performed based on the reduced-order virtual flux observer using the actual currents and the command control voltage. Moreover, source voltage sensors are replaced by a estimated flux. DC output voltage has been compensated by DC output ripple voltage estimation algorithm. The active and reactive powers estimation are performed based on the estimated flux and Phase angle. The proposed algorithm is verified through simulation and experiment.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.2
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• pp.114-120
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• 2000

Fossil power plant availability is significantly affected by gradual degradations of equipment as operation of the plant continues. It is quite important to determine whether or not to replace some equipment and when to replace the equipment. Performance calculation and analysis can provide the information. Robustness in the performance calculation can be increased by using verification ＆ validation of measured input data. We suggest new algorithm in which estimation relation for validated measurement can be obtained using correlation between measurements. Input estimation model is obtained using design data and acceptance measurement data of domestic 16 fossil power plant. The model consists of finding mostly correlated state variable in plant state and mapping relation based on the model and current state of power plant.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.4
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• pp.181-185
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• 2011

Due to the increasing of nonlinear loads such as converters and inverters connected to the electric power distribution system, and extensive application of harmonic generation sources with power electronic devices, disturbance of the electric power system and its influences on industries have been continuously increasing. Thus, it is difficult to construct accurate load model for active and reactive power in environments with harmonics. In this research, we develop current harmonics estimation method based on Extreme Learning Machine (ELM) with fast learning procedure for residential loads. Using data sets acquired from various residential loads, the proposed method has been intensively tested. As the experimental results, we confirm that the proposed method makes it possible to effective estimate current harmonics for various input voltage.

• Structural Engineering and Mechanics
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• v.34 no.1
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• pp.1-14
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• 2010

The innovative intelligent fuzzy weighted input estimation method which efficiently and robustly estimates the unknown time-varying input force in on-line is presented in this paper. The algorithm includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. To directly synthesize the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable compromise between the tracking capability and the flexibility against noises. The capability of this inverse method are demonstrated in the input force estimation cases of the plate structure system. The proposed algorithm is further compared by alternating between the constant and adaptive weighting factors. The results show that this method has the properties of faster convergence in the initial response, better target tracking capability, and more effective noise and measurement bias reduction.

• Journal of Power System Engineering
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• v.7 no.1
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• pp.74-81
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• 2003

A system identification is to estimate the mathematical model on the base of input output data and to measure the output in the presence of adequate input for the controlled system. In the traditional system control field, most identification problems have been thought as estimating the unknown modeling parameters on the assumption that the model structures are fixed. In the system identification, it is possible to estimate the true parameter values by the adjusted least squares method in the input output case of no observed noise, and it is possible to estimate the true parameter values by the total least squares method in the input output case with the observed noise. We suggest the adjusted least squares method as a consistent estimation method in the system identification in the case where there is observed noise only in the output. In this paper the adjusted least squares method has been developed from the least squares method and the efficiency of the estimating results was confirmed by the generating data with the computer simulations.

• The Transactions of the Korean Institute of Power Electronics
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• v.8 no.2
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• pp.173-179
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• 2003

In this paper, a novel on-line dc capacitance estimation method for the three-phase PWM converter is proposed. At no load, input current at a low frequency is injected, which causes dc voltage ripple. With the at voltage and current ripple components of the dc side, the capacitance can be calculated. Experimental result shows that the estimation error is less than 2%.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.109-118
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• 2023

Data-based analysis methods have become used more for estimating or predicting housing prices, and neural network models and decision trees in the field of big data are also widely used more and more. Neural network models are often evaluated to be superior to existing statistical models in terms of estimation or prediction accuracy. However, there is ambiguity in determining the input feature of the input layer of the neural network model, that is, the type and number of input features, and decision trees are sometimes used to overcome these disadvantages. In this paper, we evaluate the existing methods of using decision trees and propose the method of using decision trees to prioritize input feature selection in neural network models. This can be a complementary or combined analysis method of the neural network model and decision tree, and the validity was confirmed by applying the proposed method to house price estimation. Through several comparisons, it has been summarized that the selection of appropriate input characteristics according to priority can increase the estimation power of the model.

• Journal of Power Electronics
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• v.15 no.4
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• pp.1085-1092
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• 2015

In this paper, the expressions of the estimated information of a single-phase enhanced phase-locked loop (EPLL), when input signal contains harmonics and a DC offset while the fundamental component takes step changes, are derived. The theoretical analysis results indicate that in the estimated information, the n^th-order harmonics cause n+1^th-order periodic ripples, and the DC offset causes a periodic ripple at the fundamental frequency. Step changes of the amplitude, phase angle and frequency of the fundamental component cause a transient periodic ripple at twice the frequency. These periodic ripples deteriorate the performance of the EPLL. A hybrid filter based EPLL (HF-EPLL) is proposed to eliminate these periodic ripples. A delay signal cancellation filter is set at the input of the EPLL to cancel the DC offset and even-order harmonics. A sliding Goertzel transform-based filter is introduced into the amplitude estimation loop and frequency estimation loop to eliminate the periodic ripples caused by the residual input odd-order harmonics and step change of the input fundamental component. The parameter design rules of the two filters are discussed in detail. Experimental waveforms of both the conventional EPLL and the proposed HF-EPLL are given and compared with each other to verify the theoretical analysis and advantages of the proposed HF-EPLL.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.4
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• pp.241-247
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• 2014

This paper propose a variable parameter estimations of five-phase squirrel-cage induction motor(IM) for speed control system. In order to high performance control of AC motor using a field oriented control(FOC) and direct torque control(DTC) algorithm, there are required precise motor parameters for slip calculation, flux observer, controller gain, rotor position, speed estimation, and so on. We are suggest a analyzed estimation results of the motor parameters that developing five-phase squirrel-cage IM have a stator of concentrated winding for experimental of variable input power frequency. There are results of stator winding test, no-load test, locked-rotor test, variable actual load test, and estimated parameters of equivalent circuits using manufactured experimental apparatus by IEEE Standard Test Procedure for Polyphase Induction Motors and Generators 112-2004.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1098_1099
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• 2009

In this paper, we present the estimation of wind turbine power generation using Cascade Architectures of Fuzzy Neural Networks(CAFNN). The proposed model uses the wind speed average, the standard deviation and the past output power as input data. The CAFNN identification process uses a 10-min average wind speed with its standard deviation. The method for rule-based fuzzy modeling uses Gaussian membership function. It has three fuzzy variables with three modifiable parameters. The CAFNN's configuration has three Logic Processors(LP) that are constructed cascade architecture and an effective optimization method uses two-level genetic algorithm. First, The CAFNN is trained with one-day average input variables. Once the CAFNN has been trained, test data are used without any update. The main advantage of using CAFNN is having simple structure of system with many input variables. Therefore, The proposed CAFNN technique is useful to predict the wind turbine(WT) power effectively and hence that information will be helpful to decide the control strategy for the WT system operation and application.