• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.564-566
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• 2003

전력 계통에서 안정한 전력을 공급하는 것은 매우 중요하다. 전력 계통의 오류는 전압 및 주파수를 감시함으로써 검출 가능하다. 본 논문에서는 디지털 위상 고정 루프를 이용한 전력 계통의 주파수 측정 장치를 제안하고 이를 구현한 결과를 제시하고자 한다. 제안한 주파수 측정 장치는 위상 고정 루프의 기본요소로 구성된다. 위상분별기는 배타적 논리연산을 통해 위상오차를 검출하고 위상의 앞섬 및 뒤짐의 검출이 가능하도록 설계하였으며, 전력 계통의 주파수 동특성을 고려해서 3차의 루프 필터를 설계하였다. DCO는 출력 주파수의 분해능을 고려하여 입력 신호를 정확하게 추정할 수 있도록 설계하였다. 제안한 주파수 측정 장치의 성능을 검증하기 위하여 모의실험을 통해 주파수 변동량의 측정 범위 및 정확도를 검토하였으며, FPGA와 CPU를 포함하는 하드웨어를 구현하였다. FPGA에는 Verilog HDL로 디지털 위상 고정 루프의 위상분별기와 DCO를 구현하였으며 루프필터는 소프트웨어로 구현하였다. 제안한 디지털 위상 고정 루프의 성능 검증을 위해 정밀한 함수 발생기의 출력을 인가한 후 출력 주파수를 측정한 결과 및 전력 계통에 대한 실험 결과를 제시하였다.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.5
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• pp.694-699
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• 2012

This paper proposes a new sensorless speed control scheme of permanent magnet DC motor using a numerical model and hysteresis controller, which requires neither shaft encoder, speed estimator nor PI controllers. By supplying the identical instantaneous voltage to both model and motor in the direction of reducing torque difference, the rotor speed approaches to the model speed, namely setting value and the system can control motor speed precisely. As the numerical model whose electric parameters are the same as those of the actual motor is adopted, the armature rotating speed can be converged to the setting value by controlling torque on both sides to be equalized. And the hysteresis controller controls torque by restricting the torque errors within respective hysteresis bands, and motor torque are controlled by the armature voltage. The experiment results indicate good speed and load responses from the low speed range to the high, show accurate speed changing performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1743-1750
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• 2003

DC motor requires a rotor speed sensor for accurate speed control. The speed sensors such as resolvers and encoders are used as a speed detector, but they increase cost and size of the motor and restrict the industrial drive applications. So in these days, many papers have reported in the sensorless operation of DC motor〔3­5〕. This paper presents a new sensorless strategy using neural networks〔6­8〕. Neural network has three layers which are input layer, hidden layer and output layer. The optimal neural network structure was tracked down by trial and error, and it was found that 4­16­1 neural network structure has given suitable results for the instantaneous rotor speed. Also, learning method is very important in neural network. Supervised learning methods〔8〕 are typically used to train the neural network for learning the input/output pattern presented. The back­propagation technique adjusts the neural network weights during training. The rotor speed is gained by weights and four inputs to the neural network. The experimental results were found satisfactory in both the independency on machine parameters and the insensitivity to the load condition.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.7
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• pp.635-641
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• 2016

In order to drive a hybrid propulsion device which combines an engine and an electric propulsion unit, battery packs that contain dozens of unit cells consisting of a lithium-based battery are used to maintain the power source. Therefore, it is necessary to more strictly manage a number of battery cells at any given time. In order to manage battery cells, generally voltage, current, and temperature data under load condition are monitored from a personal computer. Other important elements required to analyze the condition of the battery are the internal resistances that are used to judge its state-of-health (SOH) and the open-circuit voltage (OCV) that is used to check the battery charging state. However, in principle, the internal resistances cannot be measured during operation because the parallel equivalent circuit is composed of internal loss resistances and capacitance. In most energy storage systems, battery management system (BMS) operations are carried out by using data such as voltage, current, and temperature. However, during operation, in the case of unexpected battery cell failure, the output voltage of the power supply can be changed and propulsion of the hybrid vehicle and vessel can be difficult. This paper covers the implementation of a high safety battery management system (HSBMS) that can estimate the OCV while the device is being driven. If a battery cell fails unexpectedly, a DC power supply with lithium iron phosphate can keep providing the load with a constant output voltage using the remainder of the batteries, and it is also possible to estimate the internal resistance.

• Composites Research
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• v.27 no.4
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• pp.141-145
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• 2014

This paper presents a structural health monitoring method using piezoelectric paint sensor designed for an impact sensor. The piezoelectric paint sensor can be flexibly deposited onto most structural surfaces in a thin form of the paint, and measure impact signals without any external device such as a power amplifier. In this study, a composite plate having four zones coated with piezoelectric paint was used for impact monitoring test. The sensitivity of the piezoelectric paint sensor was obtained by measuring the output voltages against the impact force. In addition to the sensitivity measurement, the impact position has been also estimated by comparing the output signals of the paint sensors when the impact occurs on the specimen.

• The Journal of Korean Institute of Information Technology
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• v.18 no.5
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• pp.55-64
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• 2020

Estimating the state of charge (SOC) of a battery is essential for increasing the stability and reliability of a photovoltaic system. In this study, battery SOC estimation methods were proposed using artificial neural networks (ANNs) with gradient descent (GD), Levenberg-Marquardt (LM), and scaled conjugate gradient (SCG), and an adaptive neuro-fuzzy inference system (ANFIS). The charge start voltage and the integrated charge current were used as input data and the SOC was used as output data. Four models (ANN-GD, ANN-LM, ANN-SCG, and ANFIS) were implemented for battery SOC estimation and compared using MATLAB. The experimental results revealed that battery SOC estimation using the ANFIS model had both the highest accuracy and highest convergence speed.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.37-40
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• 1999

DSP를 이용한 Brushless DC (BLDC) 모터의 초고속 센서리스 제어 기법 및 그 결과가 제시된다. 초고속 BLDC 모터의 센서리스 제어를 위해서 주 제어기로 DSP TMS320C240이 사용되며 제어 기법으로는 Pulse Amplitude Modulation(PAM) 기법이 사용된다. RAM 제어 기법을 사용함으로서 인버터는 six-step 방식으로 구동되며 속도 제어는 인버터 DC 링크 앞 단 쵸퍼의 전압 제어에 의해 이루어진다. 회전자 위치는 역기전력 센싱 방식에 의해 추정되며 TMS320C240의 Event Manager Module에 입력되어 Commutation 위치 및 속도가 계산된다. 계산된 속도는 디지털 PI 제어 알고리즘에 의해 처리되며 제어기의 출력은 쵸퍼의 duty 비를 변화시킨다. 개발된 DSP 제어 보드 및 제어 알고리즘의 성능을 시험하기 위해 실험이 수행되었으며 전체 제어 알고리즘은 DSP TMS320C240의 어셈블리 프로그램에 의해 구현된다. 결과로 최고 속도 50000 [rpm]에서 이상적인 응답 특성을 얻을 수 있었다.

• The Transactions of the Korean Institute of Power Electronics
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• v.5 no.5
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• pp.451-458
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• 2000

In wind power generating system connected in power grid, the value of stator flux has almost constant because the stator side of doubly fed induction machine(DFIM) is connected to power grid. Using the stator and rotor current, it is possible to estimate the slip angle and rotor speed. A stator flux orientation scheme and rotor slip estimator are employed to achieve control of generating power in stator side. To verify the theoretical analysis, a 5-hp DFIM prototype system and PWM power converter are built. Results of computer simulation and experiment are presented to support the discussion.

• Investigative Magnetic Resonance Imaging
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• v.13 no.1
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• pp.15-21
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• 2009

Purpose : A method to estimate a real k-space trajectory based on a circuit model of the gradient system is proposed for spiral imaging. The estimated k-space trajectory instead of the ideal trajectory is used in the reconstruction to improve the image quality in the spiral imaging. Materials and Methods : Since the gradient system has self resistance, capacitance, and inductance, as well as the mutual inductance between the magnet and the gradient coils, the generated gradient fields have delays and transient responses compared to the input waveform to the gradient system. The real gradient fields and their trajectory in k-space play an important role in the reconstruction. In this paper, the gradient system is modeled with R-L-C circuits, and real gradient fields are estimated from the input to the model. An experimental method to determine the model parameters (R, L, C values) is also suggested from the quality of the reconstructed image. Results : The gradient fields are estimated from the circuit model of the gradient system at 1.5 Tesla MRI system. The spiral trajectory obtained by the integration of the estimated gradient fields is used for the reconstruction. From experiments, the reconstructed images using the estimated trajectory show improved uniformity, reduced overshoots near the edges, and enhanced resolutions compared to those using the ideal trajectory without model. Conclusion : The gradient system was successfully modeled by the R-L-C circuits. Much improved reconstruction was achieved in the spiral imaging using the trajectory estimated by the proposed model.

• The Journal of Korean Institute of Information Technology
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• v.16 no.11
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• pp.35-41
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• 2018

Electrical impedance tomography (EIT) is a kind of nondestructive testing technique that obtains the internal resistivity distribution from the voltages measured at the electrodes located outside the area of interest. However, an image reconstruction problem in EIT has innate non-linearity and ill-posedness, so that it is difficult to obtain satisfactory reconstructed results. In general, a neural network can efficiently model the input and output relationships of a non-linear system. This paper proposes a method for estimating the position and size of a circular target using a multi-layer neural network. To verify the performance of the proposed method, neural network was trained and various computer simulations were performed and satisfactory performance was verified.