• 제목/요약/키워드: Electrical network

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신경 회로망을 이용한 DC 모터의 제어 (Control of a DC motor using Neural Networks)

  • 이화석;박준호;최영규;황창선
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1992년도 하계학술대회 논문집 A
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    • pp.239-241
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    • 1992
  • In this paper, back-propagation neural network is used for the identification and trajectory control of a DC motor. The neural network is trained to identify the unknown nonlinear dynamics of the motor and load and the trained neural network is used for speed control of the DC motor to have good performance. Simulation results show the good performance of the control system based on the neural network under arbitrarily chosen speed trajectories.

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계통운영시스템 계통해석 프로그램 정확도 향상에 관한 연구 (A Study on the Enhancement of Accuracy of Network Analysis Applications in Energy Management Systems)

  • 조윤성
    • 조명전기설비학회논문지
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    • 제29권12호
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    • pp.88-96
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    • 2015
  • This paper describes a new method for enhancing the accuracy of network analysis applications in energy management systems. Topology processing, state estimation, power flow analysis, and contingency analysis play a key factor in the stable and reliable operation of power systems. In this respect, the aim of topology processing is to provide the electrical buses and the electrical islands with the actual state of the power system as input data. The results of topology processing is used to input of other applications. New method, which includes the topology error analysis based on inconsistency check, coherency check, bus mismatch check, and outaged device check is proposed to enhance the accuracy of network analysis. The proposed methodology is conducted by energy management systems and the Korean power systems have been utilized for the test systems.

다단 신경회로망 예측제어기 개발에 관한 연구 (A Study on Development of Multi-step Neural Network Predictive Controller)

  • 배근신;김진수;이영진;이권순
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1996년도 추계학술대회 논문집 학회본부
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    • pp.62-64
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    • 1996
  • Neural network as a controller of a nonlinear system and a system identifier has been studied during the past few years. A well trained neural network identifier can be used as a system predictor. We proposed the method to design multi-step ahead predictor and multi-step predictive controller using neural network. We used the input and out put data of B system to train the NNP and used the forecasted approximat system output from NNP as B input of NNC. In this paper we used two-step ahead predictive controller to test B heating controll system and compared with PI controller.

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퍼지-뉴럴 제어 시스템을 이용한 직류 서보 전동기의 위치 및 속도 제어 (The position and Speed Control of a DC Servo-Motor Using Fuzzy-Neural Network Control System)

  • 강영호;정헌주;김만철;김낙교
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1993년도 하계학술대회 논문집 A
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    • pp.244-247
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    • 1993
  • In this paper, Fuzzy-Neural Network Control system that has the characteristic of fuzzy control to be controlled easily end the good characteristic of a artificial neural network to control the plant due to its learning is presented. A fuzzy rule to be applied is selected automatically by the allocated neurons. The neurons correspond to Fuzzy rules which ere created by a expert. To adaptivity, the more precise modeling is implemented by error beck-propagation learning of adjusting the link-weight of fuzzy membership function in Fuzzy-Neural Network. The more classified fuzzy rule is used to include the property of Dual Mode Method. To test the effectiveness of the algorithm presented above, the simulation for position end velocity of DC servo motor is implemented.

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보로노이 네트워크를 이용한 ZnO 바리스터의 전기적 특성 연구 (The Study of Electrical Characteristic of ZnO Varistor with Voronoi Network)

  • 황휘동;한세원;강형부
    • 한국전기전자재료학회:학술대회논문집
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    • 한국전기전자재료학회 1997년도 추계학술대회 논문집
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    • pp.85-89
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    • 1997
  • A microstructure of realistic ZnO varistor was constructed by Voronoi network and studied cia computer simulation. The grain size and standard deviation was calculated with new method and have good agreement with experimental data. In this network, the grain boundary conditions of three different type are randomly distributed. The three electrical boundary conditions . (1) type A junctions (high nonlinearity); (2) type B junctions (low nonlinearity); (3) type C junctions (linear with low-resistivity) are fitted from the experimental measurement. The electrical properties were studied by varying the boundary type concentration and the disorder parameter d. The shape of I-V characteristic curve of the network is affected by the type concentration and the disorder parameter has an effect on the double inflected region.

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Backward Explicit Congestion Control in Image Transmission on the Internet

  • Kim, Jeong-Ha;Kim, Hyoung-Bae;Lee, Hak-No;Nam, Boo-Hee
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2003년도 ICCAS
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    • pp.2106-2111
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    • 2003
  • In this paper we discuss an algorithm for a real time transmission of moving color images on the TCP/IP network using wavelet transform and neural network. The image frames received from the camera are two-level wavelet-trans formed in the server, and are transmitted to the client on the network. Then, the client performs the inverse wavelet-transform using only the received pieces of each image frame within the prescribed time limit to display the moving images. When the TCP/IP network is busy, only a fraction of each image frame will be delivered. When the line is free, the whole frame of each image will be transferred to the client. The receiver warns the sender of the condition of traffic congestion in the network by sending a special short frame for this specific purpose. The sender can respond to this information of warning by simply reducing the data rate which is adjusted by a back-propagation neural network. In this way we can send a stream of moving images adaptively adjusting to the network traffic condition.

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Convolutional neural network를 이용한 눈동자 모션인식 시스템 구현 (Implementation to eye motion tracking system using convolutional neural network)

  • 이승준;허승원;이희빈;유윤섭
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2018년도 춘계학술대회
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    • pp.703-704
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    • 2018
  • 본 논문은 몸을 움직이지 못하는 루게릭병 환자들을 위해 눈동자를 추적하여 의사소통 시스템에 필요한 눈동자의 위치를 파악해주는 인공신경망 설계에 대해 소개한다. Tensorflow를 이용해 신경망 생성 및 학습하고 학습된 신경망을 통하여 눈동자의 위치를 파악한다. 본 논문에서는 컨볼루션계층 2단계와 완전연결계층 2단계로 구성된 Convolution Neural Network(CNN)을 사용해서 구현했다.

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능동위상제어기를 이용한 배전선로 자율 재구성 시 순환전류 감소 기법 (Circulating Current Reduction Method during Distribution Network Dynamic Reconfiguration using Active Phase Controller)

  • 김수연;정다움;박성준;김동희
    • 전력전자학회논문지
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    • 제25권1호
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    • pp.6-12
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    • 2020
  • In recent years, the demand for the distribution of energy resource has been increasing. However, the output power is limited by the stability of the distribution network. This study proposes an active distribution network that can reconfigure the distribution line using an active phase controller. The conventional distribution network has a fixed structure, whereas the proposed active distribution network has a variable structure. Therefore, the active distribution network can increase the output power of the distribution energy resource and reduce the overload of distribution line facilities. The active phase controller has two operation modes to minimize the circulating current during dynamic reconfiguration. In this study, the voltage and current control algorithms are proposed for the active phase controller. The proposed method for the active phase controller is simulated via PSIM simulation.

신경회로망을 이용한 IPMSM 드라이브의 온라인 파라미터 추정 (On-line Parameter Estimation of IPMSM Drive using Neural Network)

  • 박기태;최정식;고재섭;이정호;김종관;박병상;정동화
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2006년도 제37회 하계학술대회 논문집 B
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    • pp.761-762
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    • 2006
  • A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

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Remote Monitoring with Hierarchical Network Architectures for Large-Scale Wind Power Farms

  • Ahmed, Mohamed A.;Song, Minho;Pan, Jae-Kyung;Kim, Young-Chon
    • Journal of Electrical Engineering and Technology
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    • 제10권3호
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    • pp.1319-1327
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    • 2015
  • As wind power farm (WPF) installations continue to grow, monitoring and controlling large-scale WPFs presents new challenges. In this paper, a hierarchical network architecture is proposed in order to provide remote monitoring and control of large-scale WPFs. The network architecture consists of three levels, including the WPF comprised of wind turbines and meteorological towers, local control center (LCC) responsible for remote monitoring and control of wind turbines, and a central control center (CCC) that offers data collection and aggregation of many WPFs. Different scenarios are considered in order to evaluate the performance of the WPF communications network with its hierarchical architecture. The communications network within the WPF is regarded as the local area network (LAN) while the communication among the LCCs and the CCC happens through a wide area network (WAN). We develop a communications network model based on an OPNET modeler, and the network performance is evaluated with respect to the link bandwidth and the end-to-end delay measured for various applications. As a result, this work contributes to the design of communications networks for large-scale WPFs.