• Journal of the Korean Regional Science Association
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• v.33 no.2
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• pp.25-37
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• 2017

The paper is basically designed to investigate how regional consumption affects to regional income in Korea by introducing a model with panel cointegration, rational expectation and FM-OLS cointegration methodology. Empirical evidence reveals that the regional income could be stimulated by manipulating the regional consumption due to the fact that current regional consumption and first-lagged regional income are positively related to the level of regional income. Although there exists a possibility to increase the regional income which is associated with a spending multiplier in the group of regions with highly calculated MPC, but not in the groups of regions with middle and low calculated MPCs. To this end, it could be tentatively concluded that market-oriented system should be implemented elaborately to enable that the spending multipliers are appropriately operated in these two groups.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1215-1219
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• 2006

Scheduling plays an important role in the process management in terms of providing profit-maximizing operation sequence of multiple orders and estimating completion times of them. In order to takes its full potential, varying conditions should be properly reflected in computing the schedule. The adjustment of scheduling decisions has to be made frequently in response to the occurrence of variations. It is often challenging because their model has to be adjusted and their solutions have to be computed within short time period. This paper employs Model Predictive Control(MPC) principles for updating the process condition in the scheduling model. The solutions of the resulting problems considering variations are computed using parametric programming techniques. The key advantage of the proposed framework is that repetition of solving similar programming problems with decreasing dimensionis avoided and all potential schedules are obtained before the execution of the actual processes. Therefore, the proposed framework contributes to constructing a robust decision-support tool in the face of varying environment. An example is solved to illustrate the potential of the proposed framework with remarks on potential wide applications.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.2
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• pp.149-155
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• 2012

This paper provides an efficient model predictive control for the output voltage control of three-phase inverter system which includes output LC filters. Use of SVPWM (Space Vector Pulse-Width-Modulation) and the rotating d-q frame is made to obtain an input constrained dynamic model of the inverter system. From the measured/estimated output current and reference output voltage, corresponding equilibrium values of the inductor current and the control input are computed. Derivation of a feasible and invariant set around the equilibrium state is made and then a receding horizon strategy which steers the current state deep into the invariant set is proposed. In order to remove offset error, use of disturbance observer is made in the form of state estimator. The efficacy of the proposed method is verified through simulations.

• Journal of Power Electronics
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• v.19 no.2
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• pp.529-539
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• 2019

The modular multilevel converter (MMC) has become a promising topology for high-voltage direct current (HVDC) transmission systems. To control a MMC system properly, the ac-side current, circulating current and submodule (SM) capacitor voltage are taken into consideration. This paper proposes a low-computation indirect model predictive control (IMPC) strategy that takes advantages of the conventional MPC and has no weighting factors. The cost function and duty cycle are introduced to minimize the tracking error of the ac-side current and to eliminate the circulating current. An optimized merge sort (OMS) algorithm is applied to keep the SM capacitor voltages balanced. The proposed IMPC strategy effectively reduces the controller complexity and computational burden. In this paper, a discrete-time mathematical model of a MMC system is developed and the duty ratio of switching state is designed. In addition, a simulation of an eleven-level MMC system based on MATLAB/Simulink and a five-level experimental setup are built to evaluate the feasibility and performance of the proposed low-computation IMPC strategy.

• Transactions on Electrical and Electronic Materials
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• v.18 no.2
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• pp.103-110
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• 2017

The number of applications of solar photovoltaic (PV) systems in power generation grids has increased in the last decade because of their ability to generate efficient and reliable power in a variety of low installation in domestic applications. Various PV converter topologies have therefore emerged, among which the modular multilevel converter (MMC) is very attractive due to its modularity and transformerless features. The modeling and control of the MMC has become an interesting issue due to the extremely large expansion of PV power plants at the residential scale and due to the power quality requirement of this application. This paper proposes a novel control method of MMC which is used to directly integrate the photovoltaic arrays with the power grid. Traditionally, a closed loop control has been used, although circulating current control and capacitors voltage balancing in each individual leg have remained unsolved problem. In this paper, the integration of model predictive control (MPC) and traditional closed loop control is proposed to control the MMC structure in a PV grid tied mode. Simulation results demonstrate the efficiency and effectiveness of the proposed control model.

• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.1
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• pp.18-26
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• 2017

This paper proposes the model predictive control with space vector modulation (SVM) method for current control of voltage-source inverter. Unlike the conventional method using a limited number of voltage vectors by switching states, the proposed method can consider various voltage vectors to identify the optimized voltage vector. The various voltage vectors are obtained by subdividing existing voltage vectors. The optimized voltage vector that minimizes the cost function is selected and applied to the inverter by using the SVM. The various voltage vectors and SVM reduce current ripples in the output AC side of the inverter compared with the conventional method. The effectiveness and performance of the proposed method are verified through simulation and experiment with a three-phase two-level voltage-source grid-connected inverter.

• Journal of The Korean Astronomical Society
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• v.37 no.5
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• pp.589-592
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• 2004

We calculate the probability to form giant radio halos (${\~}$ 1 Mpc size) as a function of the mass of the host clusters by using a Statistical Magneto-Turbulent Model (Cassano & Brunetti, these proceedings). We show that the expectations of this model are in good agreement with the observations for viable values of the parameters. In particular, the abrupt increase of the probability to find radio halos in the more massive galaxy clusters ($M {\ge} 2{\times}10^{15} M_{\bigodot}$) can be well reproduced. We calculate the evolution with redshift of such a probability and find that giant radio halos can be powered by particle acceleration due to MHD turbulence up to z${\~}$0.5 in a ACDM cosmology. Finally, we calculate the expected Luminosity Functions of radio halos (RHLFs). At variance with previous studies, the shape of our RHLFs is characterized by the presence of a cut-off at low synchrotron powers which reflects the inefficiency of particle acceleration in the case of less massive galaxy clusters.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.662-668
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• 2015

This paper presents an improved predictive control of an induction machine fed by a matrix converter using N-switching vectors as the control action during a complete sampling period of the controller. The conventional model predictive control scheme based matrix converter uses a single switching vector over the same period which introduces high torque ripple. The proposed switching scheme for a matrix converter based model predictive control of an induction machine drive selects the appropriate switching vectors for control of electromagnetic torque with small variations of the stator flux. The proposed method can reduce the ripple of the electrical variables by selecting the switching state as well as the method used in the space vector modulation techniques. Simulation results are presented to verify the effectiveness of the improved predictive control strategy for induction machine fed by a matrix converter.

• Journal of Power Electronics
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• v.15 no.4
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• pp.1006-1017
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• 2015

This paper presents a finite control set model predictive control (FCS-MPC) strategy for the AC/DC matrix converter used in grid-connected battery energy storage system (BESS). First, to control the grid current properly, the DC current is also included in the cost function because of input and output direct coupling. The DC current reference is generated based on the dynamic relationship of the two currents, so the grid current gains improved transient state performance. Furthermore, the steady state error is reduced by adding a closed-loop. Second, a Luenberger observer is adopted to detect the AC input voltage instead of sensors, so the cost is reduced and the reliability can be enhanced. Third, a switching state pre-selection method that only needs to evaluate half of the active switching states is presented, with the advantages of shorter calculation time, no high dv/dt at the DC terminal, and less switching loss. The robustness under grid voltage distortion and parameter sensibility are discussed as well. Simulation and experimental results confirm the good performance of the proposed scheme for battery charging and discharging control.

• Nuclear Engineering and Technology
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• v.39 no.1
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• pp.63-74
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• 2007

In this paper, a reconfigurable controller consisting of a normal controller and a standby controller is designed to control the thermoelectric (TE) power in the SP-100 space reactor. The normal controller uses a model predictive control (MPC) method where the future TE power is predicted by using support vector regression. A genetic algorithm that can effectively accomplish multiple objectives is used to optimize the normal controller. The performance of the normal controller depends on the capability of predicting the future TE power. Therefore, if the prediction performance is degraded, the proportional-integral (PI) controller of the standby controller begins to work instead of the normal controller. Performance deterioration is detected by a sequential probability ratio test (SPRT). A lumped parameter simulation model of the SP-100 nuclear space reactor is used to verify the proposed reconfigurable controller. The results of numerical simulations to assess the performance of the proposed controller show that the TE generator power level controlled by the proposed reconfigurable controller could track the target power level effectively, satisfying all control constraints. Furthermore, the normal controller is automatically switched to the standby controller when the performance of the normal controller degrades.