A robust automatic generation control system based on hybrid Aquila Optimizer-Sine Cosine Algorithm

•Hybrid Aquila Optimizer-Sine Cosine algorithm (HSCAO) is designed for addressing the sensitivity of the PID-AGC parameters.•The CEC-2019, and classical benchmark issues with various dimensions are utilised to validate the performance of the proposed algorithm adequately.•Statistical analysis technique is conducted using Wilcoxon's test and Friedman's test to demonstrate the supervise performance of the HSCAO optimisation.•Integral time absolute error (ITAE) is used as fitness function optimisation to determine the transiting time of the frequency.•Two-area power system network is simulated using MATLAB environment to implement the proposed AGC system. The fluctuating frequency in a power grid is the major stability challenge duo to the unpredictable power demand of costumers during the time. To address this issue, automatic generation controller (AGC) is employed. The AGC based on a proportional integral derivative (PID) approach is popularly utilised owing to its soft implementation and lower expenditure. However, it ripples to handle the standard frequency of a multi-area power grid that occurs in a competitive load-demand case, because of the high sensitivity of its uncertain parameters. In this paper, a Hybrid Aquila Optimizer-Sine Cosine algorithm (HSCAO) is designed for addressing the sensitivity of the PID-AGC parameters specifically for the multi-area power system network. The suggested algorithm is assessed based on CEC-2019, and classical benchmark issues with various dimensions to validate its performance and address the better fits of the algorithm parameters adequately. Also, a statistical analysis technique is conducted using Wilcoxon's test and Friedman test to demonstrate the supervise performance of the HSCAO optimisation regarding to other relative optimal algorithms. A two-area power system network is simulated using MATLAB environment to implement the proposed AGC system. The outcomes prove that the optimal PID-AGC method based on HSCAO technique demonstrates its ability to address the simple and complex fluctuations of load demands quickly. Also, it is the most robust to supervise the frequency response under fault condition test, resulting in, achieving the lowest ITAE index of 5.2s compared to the conventional fuzzy logic control-AGC and the conventional PID-AGC of 10.9s and 17.4s respectively.