The frequency diversion in hybrid power systems is a major challenge due to the unpredictable power generation of renewable energies. An automatic generation controller (AGC) system is utilised in a hybrid power system to correct the frequency when the power generation of renewable energies and consumers’ load demand are changing rapidly. While a neural network (NN) model based on a back-propagation (BP) training algorithm is commonly used to design AGCs, it requires a complicated training methodology and a longer processing time. In this paper, a bacterial foraging algorithm (BF) was employed to enhance the learning of the NN model for AGCs based on adequately identifying the initial weights of the model. Hence, the training error of the NN model was addressed quickly when it was compared with the traditional NN model, resulting in an accurate signal prediction. To assess the proposed AGC, a power system with a photovoltaic (PV) generation test model was designed using MATLAB/Simulink. The outcomes of this research demonstrate that the AGC of the BF-NN-based model was effective in correcting the frequency of the hybrid power system and minimising its overshoot under various conditions. The BP-NN was compared to a PID, showing that the former achieved the lowest standard transit time of 5.20 s under the mismatching power conditions of load disturbance and PV power generation fluctuation.

This study explains a new way to speed control for PMSMs based on the FOC and SVPWM techniques employed in the building of the permanent magnet synchronous motors (PMSMs). When it comes to current control, two inner and one outside feedback loops were used. Feedback control with FOPID controllers is used to optimize the performance of PMSM motor design. FOPID parameters were optimized using genetic algorithms in MATLAB/Simulink simulations. Good dynamic and static qualities are demonstrated through simulation results. There is also a comparison of PMSM PID and FOPID controllers included.