Output Power Control Using Artificial Neural Network for Switched Reluctance Generator

Abstract

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.

Integration of Switched Reluctance Generator in a Wind Energy Conversion System: An Overview of the State of the Art and Challenges

“…The power output profiles of the SRG are also influenced by a number of factors such as load current, phase voltage, phase inductance, speed, and rotor position. Many researchers have carried out extensive research into the optimization of control parameters [91][92][93][94][95][96][97][98]. Therefore, the challenge for researchers is to design an optimal control of the output power of a SRG that takes into account all influencing factors.…”