• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1073-1075
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• 1998

This paper presents the method of parameter estimation of diffusion model for monitoring Demand-Side Management Program. Bass diffusion model was applied in this paper, which has different values according to parameters ; coefficients of innovation, imitation and potential adopters. Though it is very important to estimate three parameter, there are no empirical results in practice. Thus, this paper presents the method of parameter estimation in case of few data with constraints to reduce the possibility of bad estimation. The constraints are empirical results or expert's decision. Case studies show the diffusion curves of high-efficient lighting and also forecasting of the peak value for power demand considering diffusion of high-efficient lighting, the feedback and least-square parameter estimation method used in this paper enable us to evaluate the status and forecasting of the effect of DSM program.

• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.360-364
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• 2009

This paper suggests the techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase induction machine using genetic algorithm. The parameter estimation procedure is based on the steady-state phase current versus slip and input power versus slip characteristics. The propose estimation algorithm is of non-linear kind based on selection in genetic algorithm. The machine parameters are obtained as the solution of a minimization of objective function by genetic algorithm. Simulation shows good performance of the propose procedures.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.535-535
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• 2000

This paper presents an adaptive receding horizon H$_{\infty}$ controller for the linear parameter varying systems in the deterministic environment, which combines a parameter range estimator and a robust receding horizon H$_{\infty}$ controller using the parameter bounds. Using parameter set inclusion and terminal inequality condition, the closed-loop system stability is guaranteed. It is shown that the stabilizing adaptive receding horizon H$_{\infty}$ controller guarantees the H$_{\infty}$ norm bound.

• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.149-158
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• 2009

It is important to derive reliable parameter values in the measurement-based load model development of electric power systems. However parameter estimation tasks, in practice, often face the parameter identifiability issue; whether or not the model parameters can be estimated with a given input-output data set in reliable manner. This paper introduces concepts and practical definitions of the local identifiability of model parameters. A posteriori local identifiability is defined in the sense of nonlinear least squares. As numerical examples, local identifiability of third-order induction motor (IM) model and a Z-induction motor (Z-IM) model is studied. It is shown that parameter ill-conditioning can significantly affect on reliable parameter estimation task. Numerical studies show that local identifiability can be quite sensitive to input data and a given local solution. Finally, several countermeasures are proposed to overcome ill-conditioning problem in measurement-based load modeling.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.2
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• pp.3-9
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• 2012

The paper describes a novel technique for on line parameter identification of three-phase induction motors from a single, run up to speed test. Data is sampled during this test with the normal locked rotor and synchronous speed data captured on the way to reaching the motor's rated speed. Rotor parameter variations with frequency due to skin and proximity effects and other non-linear imperfections such as heating and main flux path saturation are taken into account. This method is ideal for determining and/or verifying parameters used in high performance drives.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.101-103
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• 2016

Recently, the target object can be represented as sparse coefficient vector in visual tracking. Due to this reason, exploitation of the compressibility in the transform domain by using L1 minimization is needed. Further, L1 minimization is proposed to handle the occlusion problem in visual tracking, since tracking failures mostly are caused by occlusion. Furthermore, there is a weighted parameter in L1 minimization that influences the result of this minimization. In this paper, this parameter is analyzed for occlusion problem in visual tracking. Several coefficients that derived from median value of the target object, mean value of the arget object, the standard deviation of the target object are, 0, 0.1, and 0.01 are used as weighted parameter of L1 minimization. Based on the experimental results, the value which is equal to 0.1 is suggested as weighted parameter of L1 minimization, due to achieved the best result of success rate and precision performance parameter. Both of these performance parameters are based on one pass evaluation (OPE).

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.386-391
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• 2003

This paper focuses on robust $H_{\infty}$ control for bilinear systems with time-varying parameter uncertainties and exogenous disturbance via output feedback. $H_{\infty}$ control is achieved via separation into a $H_{\infty}$ state feedback control problem and a $H_{\infty}$ state estimation problem. The suitable robust stabilizing output feedback control law can be constructed in term of approximated solution to x-dependent Riccati equation using successive approximation technique. Also, the $H_{\infty}$ filter gain can be constructed in term of solution to algebraic Riccati equation. The output feedback control robustly stabilizes the plant and guarantees a robust $H_{\infty}$ performance for the closed-loop systems in the face of parameter uncertainties and exogenous disturbance.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.175-178
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• 1991

The drive system of servo motor is requested to have robustness of disturbance and parameter variation. However, the dynamics of PMSM drive change significantly by forced disturbance and parameter variation. Moreover, the state error caused by them should be suppressed completely and rapidly. In this paper, the vector-control system of PMSM using dual adaptive control loop is investigated. In the proposed system, linear adaptive control loop rapidly recovers the state error caused by both disturbance and parameter variation. In the dual adaptive control loop, the inner loop reduces the system sensitivity of parameter variation and disturbance, and the outer loop suppresses the state error caused by them completely. The proposed servo system is verified through a computer simulations and experimental results.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2262-2270
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• 2015

Generally, internal parameters of the motors and generators can be divided to the resistance and inductance components. They can become a cause of the changing internal parameters because they have sensitive characteristics due to external conditions. The changed parameters can generate the outputs which include error values from the speed and current controllers. Also, it can bring the temperature increase and mechanical damage to the system. Therefore, internal parameters of the motors and generators need to obtain their values according to the external conditions because it can prevent the mechanical damage caused by the changed parameters. In this paper, the off-line parameter identification method is verified using the Goertzel algorithm. The motor used in the simulation and experiments is an interior permanent magnet synchronous motor (IPMSM), and the proposed algorithm is verified by the simulation and experimental results.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1202-1211
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• 2013

This paper deals with a robust controller for an induction motor (IM) which is represented as a linear parameter varying systems. To do so linear matrix inequality (LMI) based approach and robust Lyapunov feedback are associated. This approach is related to the fact that the synthesis of a linear parameter varying (LPV) feedback controller for the inner loop take into account rotor resistance and mechanical speed as varying parameter. An LPV flux observer is also synthesized to estimate rotor flux providing reference to cited above regulator. The induction motor is described as a polytopic LPV system because of speed and rotor resistance affine dependence. Their values can be estimated on line during systems operations. The simulation and experimental results largely confirm the effectiveness of the proposed control.