TY - JOUR
AU - 
AB - Sensors and Materials, Vol. 33, No. 7 (2021) 2427–2444 2427 MYU Tokyo S & M 2628 Output Power Control Using Artic fi ial Neural Network for Switched Reluctance Generator 1* 2 1 Supat Kittiratsatcha, Paiwan Kerdtuad, and Chanin Bunlaksananusorn Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand Faculty of Engineering, Rajamangala University of Technology Isan Khonkaen Campus, Khonkaen 40000, Thailand (Received January 31, 2021; accepted June 14, 2021) Keywords: switched reluctance generator, output power control, output power estimation, conduction angle estimation, articia fi l 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 
TI - Output Power Control Using Artificial Neural Network for Switched Reluctance Generator
JO - Sensors and Materials
DO - 10.18494/sam.2021.3312
DA - 2021-07-15
UR - https://www.deepdyve.com/lp/unpaywall/output-power-control-using-artificial-neural-network-for-switched-cSU0WW7AW9
DP - DeepDyve
ER -