• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.5
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• pp.449-456
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• 2017

This study proposes a method for compensating for the commutation torque ripple of delta-connected brushless DC motors with low inductance based on a current predictions. At the commutation instant, a phase current at the next sampling period is predicted and compared with the reference phase current to determine whether torque ripples will occur or not. If the predicted current cannot reach the reference phase current, the reference current is modified and the relevant voltage reference is produced to reduce the torque ripple. The validity of the proposed method has been verified by simulation and experimental results. The commutation torque ripple has been decreased by 17.7% at 1,000 rpm and 80% load conditions.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.4
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• pp.175-185
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• 2002

In this paper, a new fully digital current control method of UPS inverter, which is based on an internal model control, is proposed. In the proposed control system, overshoots and oscillations due to the computation time-delay are compensated by explicit incorporation of the time-delay in the current control loop transfer function. The internal model controller is adopted to a second order deadbeat reference-to-output response which means that its response reaches the reference in two sampling time including computational time-delays. That is, the average current of filter capacitor is been exactly equal to the reference current with a time lag of two sampling intervals. Therefore, this method has an essentially overshoot free reference-to-output response with a minimum possible rise time. The effectiveness of the proposed control system has been verified by the simulation and experimental respectively. From the simulation and experimental results, the proposed system is achieved the robust characteristics to the calculation time delay and parameter variation as well as very fast dynamic performance, thus it can be effectively applied to the power supply for the critical load.

• Journal of Power Electronics
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• v.19 no.2
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• pp.497-508
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• 2019

The predictive speed control (PSC) strategy can realize the simultaneous control of speed and current by using one cost function. As a model-based control method, the performance of the PSC is vulnerable to model mismatches such as load torque disturbances and parameter uncertainties. To solve this problem, this paper presents a robust predictive speed control (RPSC) strategy for surface-mounted permanent magnet synchronous motor (SPMSM) drives. The proposed RPSC uses extended state observers (ESOs) to estimate the lumped disturbances caused by load torque changes and parameter mismatches. The observer-based prediction model is then compensated by using the estimated disturbances. The introduction of ESOs can achieve robustness against predictive model uncertainties. In addition, a modified cost function is designed to further suppress load torque disturbances. The performance of the proposed RPSC scheme has been corroborated by experimental results under the condition of load torque changes and parameter mismatches.

• Transactions of Materials Processing
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• v.24 no.4
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• pp.234-240
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• 2015

Multi-pass shape drawing is very important to produce steel profiles in round samples. In the current study, a process design system is developed for a multi-pass shape drawing. In general, the number of passes for a multi-pass shape drawing is 2 to 3 when the reduction ratio, drawing stress, and productivity are considered. Therefore, calculating the drawing stress and designing the intermediated die shapes are very important. In order to calculate the drawing stress, a shape drawing load prediction method is proposed using a general axisymmetric drawing load prediction model. An intermediate die shape design method is proposed using the initial and the final product shapes. Based on this analysis, a process design system is developed for multi-pass shape drawing for steel profiles. The system works with AutoCAD. The system was applied to design a shape drawing of a spline.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.995-1000
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• 2017

In this paper, we present an electrical arc detection algorithm using the mash-up analysis technique which is the core technology for the autonomous electrical safety management system(AESMS) of the multi-unit dwellings. The mash-up analysis technique analyzes the voltage, load current, zero phase current data simultaneously to judge arc faults. In order to develop the arc fault detection algorithm, the characteristics of series arc and parallel arc were analyzed. Also, we propose the mash-up analysis technique that analyzes waveforms of voltage, load current, and zero phase current at the same time. The arc fault detection algorithm was developed using the mash-up analysis technique. The developed algorithm can prevent electrical disasters in an effective way through accident prediction, and it will be used as a basic technology to introduce an autonomous electrical safety management system.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.90-96
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• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• Ocean Systems Engineering
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• v.11 no.1
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• pp.43-58
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• 2021

In the present work, a 3D numerical model is proposed to study local scouring around single vertical piers with different cross-section shapes under steady-current flow. The model solves the flow field and sediment transport processes using a coupled approach. The flow field is obtained by solving the Unsteady Reynolds Averaged Navier-Stokes (URANS) equations in combination with the k-ω SST turbulence closure model and the sediment transport is considered using both bedload and suspended load models. The proposed model is validated against the empirical measurements of local scour around single vertical piers with circular, square, and diamond cross-section shapes obtained from the literature. The measurement of scour depth in equilibrium condition for the simulations reveal the differences of 4.6%, 6.7% and 13.1% from the experimental measurements for the circular, square, and diamond pier cases, respectively. The model displayed a remarkable performance in the prediction of scour around circular and square piers where horseshoe vortices (HSVs) have a leading impact on scour progression. On the other hand, the maximum deviation was found in the case of the diamond pier where HSVs are weak and have minimum impact on the formation of local scour. Overall, the results confirm that the prediction capability of the present model is almost independent of the strength of the formed HSVs and pier cross-section shapes.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2283-2286
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• 2003

This paper presents conservation of electrical energy in building with harmonics analysis and compensation which occur in electrical system. We use load controlling and management system in order to adjust load factor of system.The maximum demand limiting and controlling are used ,then the system can acquire the prediction and compare it to the maximum demand set point.The electrical signal analysis based on FFT technique. The harmonics are compensated by using harmonic filters.This system consists computer which works as controller, processor , analysis and database unit together with digital power meter in form of multidrop network through serial communication via RS-485.The load control system uses PLC to control load via serial communication RS-485. The A/D converter is used for sampling the electrical signals via parallel port of computer.The harmonic filters are controlled by a computer.The data of measurement such as voltage, current, power, power factor, total harmonic distortion, energy, etc., can be saved as database and analysis. The load factor is adjusted by limiting and controlling maximum demand. The load factor adjustment can reduce the cost of electric consumption and energy generation together with harmonics compensation in order to increase high efficiency of electrical system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.163-172
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• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

• International Journal of Railway
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• v.5 no.1
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• pp.1-9
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• 2012

Reliability means that a product maintains its initial quality and performance at a certain period of time (time, distance, cycle etc) under given condition without failure. The given conditions include both environmental condition and operating condition. Environmental condition means a common natural environment such as temperature, humidity, vibration, and working condition means an artificial environment such as voltage, current load, place for installment, and hours of use, which occurs during the life of the product. In the field of railway vehicles, it is mandatory to use a part with the proved reliability as the extension of the life of vehicle become highly necessary. But the reliable assessment method for the reliability of the part is insufficient. If the reliability of the railway vehicle parts could be assessed by using the field data, the reliability of the entire system could also be evaluated reliably. In this study, life span of micro-switch for master controller is analyzed and prediction is performed based on its field data given by an operator considering the special circumstances of railway vehicles such as the operation of a large number of trains on the same line.