• 조명전기설비학회논문지
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• 제20권10호
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• pp.47-57
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• 2006

본 논문은 ALM-FNN 제어기와 ANN 제어기를 사용하여 SynRM 드라이브의 최대토크 제어를 제시한다. 이 제어기는 인버터의 정격 전류와 전압 제한을 고려하고 전 속도 영역에 적용된다. 각 제어모드를 위하여 최대토크를 위한 최적의 d-축 전류 $^i{_d}$를 결정한다. 제시된 제어 알고리즘은 ALM-FNN 제어기와 ANN 제어기로 SynRM 드라이브 시스템을 제어하는데 적용된다. 최대토크 제어에 의하여 제어된 동작 특성은 실험을 통하여 상세히 설명한다. 또한 본 눈문은 ALM-FNN 제어기와 ANN 제어기 결과분석을 통하여 타당성을 입증한다.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권3호
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• pp.110-114
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In this paper maximum torque control of IPMSM drive using artificial intelligent(AI) controller is proposed. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using adaptive learning mechanism fuzzy neural network(ALM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the experimental results to verify the effectiveness of AI controller.

• 한국산학기술학회논문지
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• 제17권5호
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• pp.364-371
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• 2016

본 논문에서는 차량이 주차된 지형의 조건에 따라 적용되는 도어 개폐 보조력 예측 모델을 제시하였다. 경사도, 사용자의 힘 등의 조건에 따른 개폐력 설정을 위하여 작동 보조력에 대한 학습 모델을 구현하여 비교하였고, 예측 모델의 학습을 위하여 축소모형을 제작하여 실험을 통해 학습데이터를 얻을 수 있는 실험 모델을 구성하였다. 실제 보상력 데이터를 학습, 반영하여 적정 값을 도출할 수 있는 학습 알고리즘을 개발하고, 이를 적용할 수 있는 시스템을 개발하였다. 학습 방법 중에서 인공신경망(Artificial Neural Network, ANN)과 서포트 벡터 머신(Support Vector Machine, SVM) 알고리즘을 적용하여 비교 검증하였다. 실제 측정값과 비교 검증한 결과, 차량의 도어 개폐 보조력 예측을 위해서 서포트 벡터 머신의 상대적으로 높은 적용성을 확인할 수 있었으며, 이 예측 모델을 활용하여 경사, 사용자의 힘에 따라 도어 개폐 구동 모터가 보상해야 할 적정한 힘을 예측하여 시간에 따라 구동함으로써 사용자가 평지와 같은 힘으로 문을 제어할 수 있는 시스템 구성을 제시하였다.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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• pp.72-79
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• 2008

The weak point of the artificial neural network(ANN) is that it is easy to fall in local minima when it learns too much nonlinear data. Accordingly, the classification ratio must be low. To overcome this weakness, the hybrid method has been proposed. That is, the ANN learns data selectively after detecting the defect position by the support vector machine(SVM). First, the SVM has been used for determination of the defect position and then the magnitude of the defect has been measured by the ANN. In off-design condition, the operation region of the engine is wide and the nonlinearity of learning data increases. The module system, dividing the whole operating region into reasonably small-size sections, has been suggested to solve this problem. In this study, the proposed algorithm has diagnosed the defects of triple components as well as single and dual components of the gas turbine engine in off-design condition.

• KIEAE Journal
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• 제14권3호
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• pp.71-77
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• 2014

This study aimed at developing and evaluating performance of the two logics for respectively operating two-position- and variable-heating systems. Both logics control the heating system and openings of the double skin facade buildings in an integrated manner. Artificial neural network models were applied for the predictive and adaptive controls in order to optimally condition the indoor thermal environment. Numerical computer simulation methods using the MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation) were employed for the performance tests of the logics in the test module. Analysis on the test results revealed that the variable control logic provided more comfortable and stable temperature conditions with the increased comfortable period and the decreased standard deviation from the center of the comfortable range. In addition, the amount of heat supply to the indoor space was significantly reduced by the variable control logic. Thus, it can be concluded that the optimal control method using the artificial neural network model can work more effectively when it is applied to the variable heating systems.

• 신재생에너지
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• 제8권3호
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• pp.14-22
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• 2012

Performance of photovoltaic (PV) generator systems relies on its operating conditions. Maximum power extracted from PV generators depends strongly on solar irradiation, load impedance, and ambient temperature. A most maximum power point tracking (MPPT) algorithm is based on a perturb and observe method and an incremental conductance method. It is well known the latter is better in terms of dynamics and tracking characteristics under condition of rapidly changing solar irradiation. However, in case of digital implementation, the latter has some error for determining a maximum power point. This paper presents a PID based MPPT algorithm for such PV systems. We use neural network technique for determining PID parameters by online learning approach. And we construct a boost converter to regulate the output voltage from PV generator system. Computer simulation is carried out to evaluate the proposed MPPT method and we accomplish comparative study with a perturb and observe based MPPT method to prove its superiority.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2007년도 추계학술대회 논문집
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• pp.313-318
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive fuzzy neural network controller and artificial neural network(ANN). This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using Adaptive-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper reposes speed control of IPMSM using Adaptive-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is a lied to IPMSM drive system controlled Adaptive-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the Adaptive-FNN and ANN controller.

• 전기전자학회논문지
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• 제26권3호
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• pp.364-372
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• 2022

본 논문에서는 FMCW(frequency modulated continuous wave) 레이다 센서를 활용한 사람과 사물을 분류하는 시스템 설계 및 구현 결과를 제시한다. 해당 시스템은 다중 객체 탐지를 위한 레이다 센서 신호처리 과정과 객체를 사람 및 사물로 분류하는 딥러닝 과정을 수행한다. 딥러닝의 경우 높은 연산량과 많은 양의 메모리를 요구하기 때문에 경량화가 필수적이다. 따라서 CNN (convolution neural network) 연산을 이진화하여 동작하는 BNN (binary neural network) 구조를 적용하였으며, 실시간 동작을 위해 하드웨어 가속기를 설계하고 FPGA 보드 상에서 구현 및 검증하였다. 성능 평가 및 검증 결과 90.5%의 다중 객체 구분 정확도, CNN 대비 96.87% 감소된 메모리 구현이 가능하며, 총 수행 시간은 5ms로 실시간 동작이 가능함을 확인하였다.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.689-700
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• 2017

This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.

• 조명전기설비학회논문지
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• 제23권8호
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• pp.57-66
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• 2009

종래의 고정된 이득을 가진 PI 제어기는 지령속도, 부하변화 등과 같은 파라미터 변동에 대해서 매우 민감하다. IPMSM 드라이브의 정확한 속도제어는 비선형적인 전자기적 발생저항뿐만 아니라 회전자 속도와 권선저항사이의 비선형적 관계 때문에 복잡한 문제점이 있다. 따라서 광범위한 동작상태에서 최적 제어를 위해 PI 제어기의 이득값을 실시간으로 조절해야한다. 본 논문은 FNN과 ALM을 이용하여 IPMSM 드라이브의 HIPI 제어기를 제시한다. 제시된 제어기는 ANN을 이용하여 속도를 추정하고, 시스템 외란에 대해서 IPMSM 드라이브의 고성능 속도제어를 제시한다. PI 제어기의 이득값은 모든 동작상태에서 ALM-FNN에 의해 최적화 되어진다. 제시된 제어기는 다양한 동작상태에 대한 분석을 통해 타당성을 입증한다.