• Smart Structures and Systems
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• 제18권3호
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• pp.449-470
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• 2016

Energy harvesting is an emerging technique that extracts energy from surrounding environments to power low-power devices. For example, it can potentially provide sustainable energy for wireless sensing networks (WSNs) or structural control systems in civil engineering applications. This paper presents a comprehensive study on harvesting energy from earthquake-induced structural vibrations, which is typically of low frequency, to power WSNs. A macroscale pendulum-type electromagnetic harvester (MPEH) is proposed, analyzed and experimentally validated. The presented predictive model describes output power dependence with mass, efficiency and the power spectral density of base acceleration, providing a simple tool to estimate harvested energy. A series of shaking table tests in which a single-storey steel frame model equipped with a MPEH has been carried out under earthquake excitations. Three types of energy harvesting circuits, namely, a resistor circuit, a standard energy harvesting circuit (SEHC) and a voltage-mode controlled buck-boost converter were used for comparative study. In ideal cases, i.e., resistor circuit cases, the maximum electric energy of 8.72 J was harvested with the efficiency of 35.3%. In practical cases, the maximum electric energy of 4.67 J was extracted via the buck-boost converter under the same conditions. The predictive model on output power and harvested energy has been validated by the test data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전력기술부문
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• pp.396-398
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• 2006

Predictive maintenance is not an unfamiliar concept because it has been used to predict the failures of electrical equipment such as transformers, motors and so on. By thoroughly monitoring the status of individual equipment and tracing how the various characteristics change over time, we can be aware of its exact condition and prevent the impending failure by taking appropriate actions. In this paper, I will extend the concept of predictive maintenance for individual electrical equipment to the power distribution system and show how to use the data obtained from power quality monitors to improve the power system.

• 제어로봇시스템학회논문지
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• 제4권4호
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• pp.543-550
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• 1998

From the power plant in a steel plant, environment pollutants such as $SO_x$, $NO_x$, CO and $CO_2$ are emitted by combustion reactions of the fuels which are by-product gases, oil and liquefied natural gas(LNG). To reduce the amounts of the pollutants, it is important to build a predictive model for the emission of the pollutants. In this paper, model that predict the amounts of generated pollutants for the used fuel is developed by using Gibbs free energy minimization method[1] with the temperature correction technique. For some data set, the calculation results from this model are compared with the real emission amounts of $SO_x$, $NO_x$, and the result of the calculation by both ASPEN PLUS which is a commercial simulation software. This model shows good results and can be applied to other power plants.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2015년도 전력전자학술대회 논문집
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• pp.522-523
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• 2015

This paper proposes a deadbeat current controller using predictive current method for three-phase shunt active power filter (SAPF). In proposed controller, the compensating current reference which is necessary for the deadbeat control is estimated by using the predictive current method. This method can improve the accuracy of compensation and ensure the fast dynamic response of SAPF. Simulations have been carried out to demonstrate the perfomance of SAPF using proposed control method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 추계학술대회 논문집 학회본부
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• pp.138-140
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• 1994

In this paper, a three-phase active power filter using voltage- source PWM converter is designed to eliminate the harmonics and compensate the reactive power in the ac side. The predictive current control method is adopted, which provides constant switching frequency and low current ripple but has inherently one sampling error between the command and the actual current. Here we propose the algorithm which corrects this delay time. The converter voltage obtained from this current control can be accomplished by the space vector modulation method at a voltage-type converter. All control sequences of active filter is executed by a DSP which is designed to calculate floating points at very hight speed. Finally, the validity of this filter using the predictive current control method is demonstrated through experimental results.

• Journal of Power Electronics
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• 제18권5호
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• pp.1398-1408
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• 2018

In order to further improve the steady-state control performance of model predictive torque control (MPTC), a double-vector-based model predictive torque control without a weighting factor is proposed in this paper. The extended voltage vectors synthesized by two basic voltage vectors are used to increase the number of feasible voltage vectors. Therefore, the control precision of the torque and the stator flux along with the steady-state performance can be improved. To avoid testing all of the feasible voltage vectors, the solution of deadbeat torque control is calculated to predict the reference voltage vector. Thus, the candidate voltage vectors, which need to be evaluated by a cost function, can be reduced based on the sector position of the predicted reference voltage vector. Furthermore, a cost function, which only includes a reference voltage tracking error, is designed to eliminate the weighting factor. Moreover, two voltage vectors are applied during one control period, and their durations are calculated based on the principle of reference voltage tracking error minimization. Finally, the proposed method is tested by simulations and experiments.

• Journal of Power Electronics
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• 제14권6호
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• pp.1178-1188
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• 2014

This study proposes a finite-state model predictive controller to regulate the load current and balance the DC-link capacitor voltages of a four-leg neutral-point-clamped converter. The discrete-time model of the converter, DC-link, inductive filter, and load is used to predict the future behavior of the load currents and the DC-link capacitor voltages for all possible switching states. The switching state that minimizes the cost function is selected and directly applied to the converter. The cost function is defined to minimize the error between the predicted load currents and their references, as well as to balance the DC-link capacitor voltages. Moreover, the current regulation performance is improved by using a two-step prediction horizon. The feasibility of the proposed predictive control scheme for different references and loads is verified through real-time implementation on the basis of dSPACEDS1103.

• 대한원격탐사학회지
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• 제33권1호
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• Journal of Power Electronics
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• 제17권6호
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• pp.1587-1599
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• 2017

This paper proposes a real-time implementation of an optimal operation of a double stage grid connected wind power system incorporating an active power filter (APF). The system is used to supply the nonlinear loads with harmonics and reactive power compensation. On the generator side, a new adaptive neuro fuzzy inference system (ANFIS) based maximum power point tracking (MPPT) control is proposed to track the maximum wind power point regardless of wind speed fluctuations. Whereas on the grid side, a modified predictive current control (PCC) algorithm is used to control the APF, and allow to ensure both compensating harmonic currents and injecting the generated power into the grid. Also a type 2 fuzzy logic controller is used to control the DC-link capacitor in order to improve the dynamic response of the APF, and to ensure a well-smoothed DC-Link capacitor voltage. The gained benefits from these proposed control algorithms are the main contribution in this work. The proposed control scheme is implemented on a small-scale wind energy conversion system (WECS) controlled by a dSPACE 1104 card. Experimental results show that the proposed T2FLC maintains the DC-Link capacitor voltage within the limit for injecting the power into the grid. In addition, the PCC of the APF guarantees a flexible settlement of real power exchanges from the WECS to the grid with a high power factor operation.

• 전력전자학회논문지
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• 제23권5호
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• pp.359-365
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• 2018

This study introduces a model predictive control method for controlling a cascaded flying capacitor multilevel rectifier used as an AC-DC rectifier of a solid-state transformer for DC distribution systems. The proposed method reduces the number of states that need to be considered in model predictive control by separately controlling input current, output DC link voltage, and flying capacitor voltage. Thus, calculation time is shortened to facilitate the level expansion of the cascaded flying capacitor multilevel rectifier. The selection of weighting factors did not present difficulties because the weighting factors in the cost function of the conventional model predictive control are not used. The effectiveness of the proposed method is verified through computer simulation using powersim and experiment.