Kittiratsatcha, Supat

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Kittiratsatcha, Supat
Alternative Name
Kittiratsatcha, S.
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Email
supat.ki@kmitl.ac.th

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    Item type:Publication,
    Modeling of a switched reluctance generator using cubic spline coefficients on the phase flux linkage, inductance and torque equations
    (2015-01-01)
    Kerdtuad, Paiwan
    ;
    Kittiratsatcha, Supat
    This paper presents a dynamic modeling method for a switched reluctance generator (SRG) by which cubic spline coefficients are applied to the phase flux linkage, phase inductance and electromagnetic torque equations. To obtain the cubic spline coefficients, the flux linkage data of the SRG are first determined by a finite element analysis (FEA) prior to fitting into a third order polynomial equation to derive the curve fitting flux linkage data. In addition, the accuracy of the curve fitting data is verified by comparing with the FEA flux linkage data. Then, the cubic spline coefficients are applied to the proposed dynamic model of the SRG to simulate the machine behaviors. The simulations were carried out in a single pulse mode with fixed conduction angles at a rotation speed lower than, equal to and higher than a based speed of 6000 rpm. This research also presents the experimental results of an 8/6 SRG based on a TMS320F2812 DSP drive system, including the phase voltage, dc-link voltage, phase current, dcload current waveforms, as well as the output power-speed characteristics. The simulation and experimental results are compared to verify the accuracy of the proposed model.
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    Item type:Publication,
    Output power control using artificial neural network for switched reluctance generator
    (2021-01-01)
    Kittiratsatcha, Supat
    ;
    Kerdtuad, Paiwan
    ;
    Bunlaksananusorn, Chanin
    We propose an output power control of a variable-speed switched reluctance generator (SRG) by implementing an artificial neural network (ANN) in the control loop. In the high-speed operation with single pulse mode, the phase current waveform, and subsequently, the output power, depend on the conduction angles. The conduction angles, i.e., the turn-on and turn-off angles, can be determined by the proposed method using an ANN. A dynamic model of an SRG with eight stator poles and six rotor poles is used for simulation to obtain the output power profiles, which subsequently become the ANN training data. The inputs of the ANN are the reference value of the output power and the rotor speeds, while the outputs of the ANN are the turn-off and turn-on angles. The control algorithm is implemented by integrating the trained data into the dynamic model using MATLAB. The experimental setup of the SRG is implemented using a digital signal processor (DSP) to control the two-switches-per-phase drive system, which includes highly accurate phase current and dc-link voltage sensor circuits. The trained biases and weights of the ANN are also coded in the DSP. To validate the proposed method, comparisons are made between simulation and experimental results.