Fuzzy Logic Rotor Position Estimation Based Switched Reluctance Motor DSP Drive With Accuracy Enhancement

Cheok, Adrian David; Zhongfang, Wang

doi:10.1109/tpel.2005.850958

Cited by 50 publications

(18 citation statements)

References 31 publications

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“…However, the position sensor system investigated in this paper is three optical encoders based measurement system, which may limit the application scope of this approach. Despite few investigations on position sensor FDD, the rotor position estimation of SRM from which the sensor health status can be inferred has been studied a lot [15][16][17][18][19][20][21][22][23][24][25][26][27][28]. One possible solution for position sensor FDD in SRM which is widely used in control theory community is to extract and analyze the residual between the actual output and the estimated output if this estimated value is accurate enough [29].…”

Section: Introductionmentioning

confidence: 99%

“…In order to overcome these shortcomings, plenty of nonlinear approximation solutions have been developed in the last decades based on the basic idea of flux linkage-currentangle characteristics of SRM. These solutions include fuzzy logic [17][18][19][20][21], Back-Propagation (BP) neural networks, Radial Basis Function (RBF) neural networks, adaptive neuro-fuzzy inference system [22][23][24][25]. The above methods substitute fuzzy or neural network models for the 2D lookup tables, which may release large amount of memory.…”

Section: Introductionmentioning

confidence: 99%

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A Fault Diagnostic Method for Position Sensor of Switched Reluctance Wind Generator

Wang¹,

Liu²,

Liu³

et al. 2016

Journal of Electrical Engineering and Technology

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-Fast and accurate fault diagnosis of the position sensor is of great significance to ensure the reliability as well as sensor fault tolerant operation of the Switched Reluctance Wind Generator (SRWG). This paper presents a fault diagnostic scheme for a SRWG based on the residual between the estimated rotor position and the actual output of the position sensor. Extreme Learning Machine (ELM), which could build a nonlinear mapping among flux linkage, current and rotor position, is utilized to design an assembled estimator for the rotor position detection. The data for building the ELM based assembled position estimator is derived from the magnetization curves which are obtained from Finite Element Analysis (FEA) of an SRWG with the structure of 8 stator poles and 6 rotor poles. The effectiveness and accuracy of the proposed fault diagnosis method are verified by simulation at various operating conditions. The results provide a feasible theoretical and technical basis for the effective condition monitoring and predictive maintenance of SRWG.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Fault Diagnostic Method for Position Sensor of Switched Reluctance Wind Generator

Wang¹,

Liu²,

Liu³

et al. 2016

Journal of Electrical Engineering and Technology

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“…In the literature, many studies can be found about obtaining rotor position by using fuzzy logic method without sensor [13][14][15]. In this method, in order to find rotor position, input currents and magnetic flux are needed to be measured with fuzzy rule-based SRM model.…”

Section: Introductionmentioning

confidence: 99%

Switched reluctance motor control without position sensor by using data obtained from finite element method in artificial neural network

2015

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Switched reluctance motors (SRMs) require sensitive rotor information in order to work sensitively and properly and especially for the commutation of phase currents. Position information is generally acquired from a sensor which is generally placed into the stator or connected to the rotor. However, sensors have disadvantages in terms of cost, safety, complexity and volume. In order to remove these disadvantages, recent studies concentrate on SRMs working without a position sensor. In this study control methods working without a position sensor are examined, the applied method is presented comprehensively. The education and test data of the artificial neural network is not obtained from simulation or experimental results; it is obtained from magnetic analyses carried out in ANSYS 10 and the results are used in position speed and current sensorless control of SRM. The validity of proposed method is demonstrated by using DS 1103.

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“…Many of these sensorless techniques use the indirect rotor position estimation such as Flux-Current Detection Technique, Current Gradient Method, Chopping Current Detection Technique [9][10][11]. Furthermore, state observer, fuzzy logic and artificial neural network are implemented with the capability of operating over a wide speed range using only terminal voltage and current of the machine [12][13][14][15][16][17]. However, these techniques have been exhibited with their advantages and drawbacks depending on the typical operating principles.…”

Section: Introductionmentioning

confidence: 99%

Rotor Position Estimation Strategy Using Artificial Neural Network for a Novel Design Transverse Flux Machine

Türker¹,

Kuyumcu²

2015

Journal of Electrical Engineering and Technology

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-The E-Core Transverse Flux Machine is a different design of transverse flux machines combined with reluctance principle. Determination of the rotor position is important for the movement of the ETFM by switching the phase currents in synchronism with the inductance regions of the stator windings. It is the first time that rotor position estimation based on Artificial Neural Network (ANN) is purposed to eliminate the position sensor for the ETFM. Simulation and experimental tests are demonstrated for the feasibility of the proposed estimation algorithm for the exercise bike application of the ETFM.

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Fuzzy Logic Rotor Position Estimation Based Switched Reluctance Motor DSP Drive With Accuracy Enhancement

Cited by 50 publications

References 31 publications

A Fault Diagnostic Method for Position Sensor of Switched Reluctance Wind Generator

A Fault Diagnostic Method for Position Sensor of Switched Reluctance Wind Generator

Switched reluctance motor control without position sensor by using data obtained from finite element method in artificial neural network

Rotor Position Estimation Strategy Using Artificial Neural Network for a Novel Design Transverse Flux Machine

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