Real-Time Optimization for an AVR System Using Enhanced Harris Hawk and IIoT

Recently, several research studies have used standard metaheuristic optimization algorithms rather than traditional algorithms and the Ziegler-Nichols (Z-N) method for tuning PID controller parameters. However, these studies have directly implemented these algorithms in order to configure the cascade control system one time. This paper presents a novel realtime monitoring and optimization architecture based on the Enhanced Harris Hawk Algorithm (EHHOA) and the Industrial Internet of Things (IIoT) for tuning the PID controller parameters for an Automatic Voltage Regulator (AVR) system. The EHHOA is based on a Chaotic map and an opposition-based learning technique that is linked to the IIoT layers. The proposed algorithm was implemented through Simulink in the MATLAB environment and it was compared with the Z-N method, the classical HHO/PID algorithm and the PSO/PID algorithm. The simulation results show that the proposed algorithm managed to enhance tuning with an insignificant difference in comparison with the other employed algorithms and EHHOA gave satisfactory results in adjusting the parameters of the PID controller, especially in IIoT real-time scenarios.