Neural Network Based Fractional Order PID Controller for Harmonic Mitigation of Induction Motor Drive System - Workagegn Tatek Asfu; Solomon Feleke Aklilu; Daniel Abebe Beyene (International Journal of Electrical Power and Machine Systems, 2024)
Torque produced in IM (induction Motor) is collected fundamental torque, however, due to core saturation, air gap irregularity, and winding distribution; stator and rotor slotting harmonics torque are produced. This reduces the quality of the power system, life span, and performance of the motor and the controller device. In this paper, a neural network, based fractional order proportional integral derivative (NNFOPID) controller is designed to compensate the harmonics of the induction motor driving system. FOPID controller parameters have been tuned automatically based on the delta-learning rule with sigmoid activation function. The active shunt capacitor is directly connected to a five-level three-phase inverter directly controlled by sinusoidal pulse width modulation (SPWM). Based on the reference and measured harmonics error, the controller parameter of FOPID is tuned using NN (neural network) delta learning algorithm. The design of discrete type PI speed and current vector controller followed by space vector pulse width modulation (SVPWM) is designed to track the actual speed of IM to the reference speed. The IM modeling and power electronics driving the system within its harmonics effect was analyzed and discussed. In addition, the effect of current and voltage harmonics with different conditions is illustrated. For this NNFOPID controller parameter a tuning was designed, and MATLAB simulations check the system performance. The result shows that the current harmonic and voltage harmonic were reduced to 2.79%, 12.12%, respectively.
Keywords: Fractional order PID; Harmonic mitigation; Neural network; Vector control; five level three phase inverters