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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials, Volume 33, Number 7(2) (2021)
Copyright(C) MYU K.K.
pp. 2427-2444
S&M2628 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2021.3312
Published: July 15, 2021

Output Power Control Using Artificial Neural Network for Switched Reluctance Generator [PDF]

Supat Kittiratsatcha, Paiwan Kerdtuad, and Chanin Bunlaksananusorn

(Received January 31, 2021; Accepted June 14, 2021)

Keywords: switched reluctance generator, output power control, output power estimation, conduction angle estimation, artificial neural network

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.

Corresponding author: Supat Kittiratsatch


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Cite this article
Supat Kittiratsatcha, Paiwan Kerdtuad, and Chanin Bunlaksananusorn, Output Power Control Using Artificial Neural Network for Switched Reluctance Generator, Sens. Mater., Vol. 33, No. 7, 2021, p. 2427-2444.



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