• Title/Summary/Keyword: On-Line Parameter Estimation

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Sensorless IPMSM Control Based on an Extended Nonlinear Observer with Rotational Inertia Adjustment and Equivalent Flux Error Compensation

  • Mao, Yongle;Yang, Jiaqiang;Yin, Dejun;Chen, Yangsheng
    • Journal of Power Electronics
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    • v.16 no.6
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    • pp.2150-2161
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    • 2016
  • Mechanical and electrical parameter uncertainties cause dynamic and static estimation errors of the rotor speed and position, resulting in performance deterioration of sensorless control systems. This paper applies an extended nonlinear observer to interior permanent magnet synchronous motors (IPMSM) for the simultaneous estimation of the rotor speed and position. Two compensation methods are proposed to improve the observer performance against parameter uncertainties: an on-line rotational inertia adjustment approach that employs the gradient descent algorithm to suppress dynamic estimation errors, and an equivalent flux error compensation approach to eliminate static estimation errors caused by inaccurate electrical parameters. The effectiveness of the proposed control strategy is demonstrated by experimental tests.

On-line Parameter Estimation of Interior Permanent Magnet Synchronous Motor using an Extended Kalman Filter

  • Sim, Hyun-Woo;Lee, June-Seok;Lee, Kyo-Beum
    • Journal of Electrical Engineering and Technology
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    • v.9 no.2
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    • pp.600-608
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    • 2014
  • This paper presents estimation of d-axis and q-axis inductance of an interior permanent magnet synchronous motor (IPMSM) by using an extended Kalman filter (EKF). The EKF is widely used for control applications including the motor sensorless control and parameter estimation. The motor parameters can be changed by temperature and air-gap flux. In particular, the variation of the inductance affects torque characteristics like the maximum torque per ampere (MTPA) control. Therefore, by estimating the parameters, it is possible to improve the torque characteristics of the motor. The performance of the proposed estimator is verified by simulations and experimental results based on an 11kW PMSM drive system.

Resonant Frequency Estimation of Reradiation Interference at MF from Power Transmission Lines Based on Generalized Resonance Theory

  • Bo, Tang;Bin, Chen;Zhibin, Zhao;Zheng, Xiao;Shuang, Wang
    • Journal of Electrical Engineering and Technology
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    • v.10 no.3
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    • pp.1144-1153
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    • 2015
  • The resonant mechanism of reradiation interference (RRI) over 1.7MHz from power transmission lines cannot be obtained from IEEE standards, which are based on researches of field intensity. Hence, the resonance is ignored in National Standards of protecting distance between UHV power lines and radio stations in China, which would result in an excessive redundancy of protecting distance. Therefore, based on the generalized resonance theory, we proposed the idea of applying model-based parameter estimation (MBPE) to estimate the generalized resonance frequency of electrically large scattering objects. We also deduced equation expressions of the generalized resonance frequency and its quality factor Q in a lossy open electromagnetic system, i.e. an antenna-transmission line system in this paper. Taking the frequency band studied by IEEE and the frequency band over 1.7 MHz as object, we established three models of the RRI from transmission lines, namely the simplified line model, the tower line model considering cross arms and the line-surface mixed model. With the models, we calculated the scattering field of sampling points with equal intervals using method of moments, and then inferred expressions of Padé rational function. After calculating the zero-pole points of the Padé rational function, we eventually got the estimation of the RRI’s generalized resonant frequency. Our case studies indicate that the proposed estimation method is effective for predicting the generalized resonant frequency of RRI in medium frequency (MF, 0.3~3 MHz) band over 1.7 MHz, which expands the frequency band studied by IEEE.

RC Circuit Parameter Estimation for DC Electric Traction Substation Using Linear Artificial Neural Network Scheme (선형인공신경망을 이용한 직류 전철변전소의 RC 회로정수 추정)

  • Bae, Chang Han;Kim, Young Guk;Park, Chan Kyoung;Kim, Yong Ki;Han, Moon Seob
    • Journal of the Korean Society for Railway
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    • v.19 no.3
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    • pp.314-323
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    • 2016
  • Overhead line voltage of DC railway traction substations has rising or falling characteristics depending on the acceleration and regenerative braking of the subway train loads. The suppression of this irregular fluctuation of the line voltage gives rise to improved energy efficiency of both the railway substation and the trains. This paper presents parameter estimation schemes using the RC circuit model for an overhead line voltage at a 1500V DC electric railway traction substation. A linear artificial neural network with a back-propagation learning algorithm was trained using the measurement data for an overhead line voltage and four feeder currents. The least square estimation method was configured to implement batch processing of these measurement data. These estimation results have been presented and performance analysis has been achieved through raw data simulation.

A Study on the Defection of Arcing Faults in Transmission Lines and Development of Fault Distance Estimation Software using MATLAB (MATLAB을 이용한 송전선로의 아크사고 검출 및 고장거리 추정 소프트웨어 개발에 관한 연구)

  • Kim, Byeong-Cheon;Park, Nam-Ok;Kim, Dong-Su;Kim, Gil-Hwan
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.51 no.4
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    • pp.163-168
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    • 2002
  • This paper present a new verb efficient numerical algorithm for arcing faults detection and fault distance estimation in transmission line. It is based on the fundamental differential equations describing the transients on a transmission line before, during and alter the fault occurrence, and on the application of the "Least Error Squares Technique"for the unknown model parameter estimation. If the arc voltage estimated is a near zero, the fault is without arc, in other words the fault is permanent fault. If the arc voltage estimated has any high value, the faust is identified as an fault, or the transient fault. In permanent faults case, fault distance estimation is necessary. This paper uses the model of the arcing fault in transmission line using ZnO arrestor and resistance to be implemented within EMTP. One purpose of this study is to build a structure for modeling of arcing fault detection and fault distance estimation algorithm using Matlab programming. In this paper, This algorithm has been designed in Graphic user interface(GUI).

Neural Network for on-line Parameter Estimation of IPMSM Drive (IPMSM 드라이브의 온라인 파라미터 추정을 위한 신경회로망)

  • 이홍균;이정철;정동화
    • The Transactions of the Korean Institute of Electrical Engineers B
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    • v.53 no.5
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    • pp.332-337
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    • 2004
  • 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.

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

  • Choi, Jung-Sik;Ko, Jae-Sub;Chung, Dong-Hwa
    • Journal of Institute of Control, Robotics and Systems
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    • v.13 no.5
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    • pp.429-433
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    • 2007
  • 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 ststor 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.

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

  • Choi, Jung-Sik;Ko, Jae-Sub;Lee, Jung-Ho;Kim, Jong-Kwan;Park, Ki-Tae;Park, Byung-Sang;Chung, Dong-Hwa
    • Proceedings of the KIEE Conference
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    • 2006.04a
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    • pp.207-209
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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 ststor 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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A Time-of-arrival Estimation Technique for Ultrawide Band Indoor Wireless Localization System (초광대역 방식의 실내 무선 위치인식 시스템에 적합한 도착시간 추정 알고리즘)

  • Lee, Yong-Up
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.34 no.8C
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    • pp.814-821
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    • 2009
  • In an ultrawide band (UWB) indoor wireless localization, time of arrival (TOA) parameter estimation techniques have some difficulties in acquiring a reasonable TOA estimate because of the clustered multipath components overlapping or random time intervals mainly due to non line-of-sight (NLOS) environment. In order to solve that problem and achieve an excellent UWB indoor wireless localization, we propose a UWB signal model and a robust TOA parameter estimation technique that has little effect on the clustered problems unlike the conventional technique. Through simulation studies, the validity of the proposed model and the TOA estimation technique are examined. The performance of estimation error is also analyzed.

A Study on the Design of Fatigue Strength using Fractal Character of Surface Micro-crack (표면미소균열의 프랙탈 특성을 이용한 피로강도설계에 관한 연구)

  • Cho, Seok-Swoo;Joo, Won-Sik
    • Journal of the Korean Society for Precision Engineering
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    • v.16 no.12
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    • pp.143-151
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    • 1999
  • The shape of surface micro-crack is very irregular due to nonhomogeneous microstructure but is very important in respect to qualitative estimation of fatigue life. Fractal geomety can quantify the shape of surface mciro-crack. Fractal dimension is measured for surface micro-cracks with coast line and box counting method and estimates cycle ration in Al 2024-T3. The average fractal dimension $D_{favg}$ of surface micro-cracks has 3-parameter weibull distribution and location parameter is nearly constant but shape parameter decreases as cycle ration increases. The fractal dimension by coast line method is measured for individual surface micro-crack but the fractal dimension by box countin method is measured for all the surface micro-cracks under sampling area. Therefore, This paper shows fractal dimension $D_{fb}$ can predict cycle ratio $N/N_f$ more convenient than fractal dimension $D_{favg}$.

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