In this article, a novel methodology is proposed by utilizing a technique which, in light of the change in the African vulture optimization known as Sine Cosine, adopted an African vulture optimization algorithm (SCaAVOA)-based tilt integral derivative (TID) regulator for the load frequency control (LFC) of a five-area power system with multi-type generations. At first, the execution of the Sine Cosine-adopted calculation is tried by contrasting it with the standard AVOA calculation while considering different standard benchmark functions. To demonstrate the superiority of the proposed SCaAVOA algorithm, the results are contrasted using different standard approaches. In the next stage, the proposed method is used in a five-area thermal power system and is likewise applied to a five-area, ten-unit system comprising different conventional sources as well as some renewable energy sources. The performance analysis of the planned regulator is completed for various system boundaries and loading conditions. It is seen that the said regulator is more viable in comparison to the other standard controllers.

Nowadays, the operation and control of power systems are a big challenge. An essential part of the power system (PS) control is load frequency control (LFC). Different secondary controllers are implemented for the frequency control problem. Hence, cascaded two-degree freedom and a tilt-integral-derivative controller having a filter (2DOFTIDF) are intended in this paper and implemented for load frequency control. In order to determine the efficiency of the 2DOFTIDF controller, a well-known non-reheat thermal system with/without a governor dead band is considered. A new whale optimization algorithm (WOA) is used to enhance the suggested controller parameters. The predominance of the presented method is exhibited by comparing the consequences with different heuristic techniques tuned to controllers published recently. Further, the simulation results for two test cases indicate that system enactments are enhanced by introducing the suggested controller and are also best suited in the presence of system nonlinearity. Finally, random load fluctuation along with noise and changing the system parameters are also used to determine the reliability of the suggested controller. Compared to the WOA-tuned TIDF controller, the settling time of ΔF₁, ΔF_2, and ΔP_Tie is improved by 45.45%, 56.77%, and 20.26%, respectively, with the WOA-tuned 2DOFTIDF controller and by 40%, 48.27%, and 20%, respectively, with the DE-tuned TIDF controller. Experimental validation using the hardware-in-the-loop real-time simulation based on OPAL-RT has been carried out to confirm the viability of the proposed approach.

Renewable generation uncertainty, dynamic load change, and system parameter variation play a significant role in the performance degradation of non‐linear multi‐microgrid (MMG) systems. As a result, intelligent control becomes the need of the hour for assisting superlative attribute‐ based consistent electric power. The application of marine predator algorithm (MPA)‐based cascaded PD‐(1+PI) controller for Automatic Generation Control (AGC) of MMG system is a novel work. A maiden attempt of the MPA is proposed to optimize the parameters of the cascaded PD‐(1+PI) controller using the integral time absolute error criterion. To demonstrate its superiority, the proposed algorithm is compared to the genetic algorithm, differential evolution, and grey wolf optimization. MPA then applied to conventional controller PID, cascaded PD‐PI controller and proposed PD‐(1+PI) controller for frequency control in multi‐microgrid system. The robustness of the suggested controller is verified over PID and PD‐PI controller by taking step and random load perturbation and integrating the renewable sources like solar and wind with their uncertain nature. The simulation of the investigated interconnected microgrid is carried out in MATLAB/SIMULINK environment. Finally, detailed simulation and hardware in the loop experimental results are presented to confirm the practicality of the proposed approach.

The existence of low-frequency oscillations in power systems is the cause of power angle instability, limiting the transmission of maximum tie-line power. One of the effective ways to improve the stability limits is by installing a power system stabilizer and supplementary excitation control to augment with an automatic voltage regulator (AVR) supplemental feedback stabilizing signal. This paper proposes a new strategy for simultaneously tuning the power system stabilizer (PSS) and FACTS controller, considering time delays. The design of the proposed controller is modeled as an optimization problem, and the parameters of the controller are optimized through the grasshopper optimization algorithm (GOA). The suggested controller’s efficacy is evaluated for both single-machine infinite bus systems and multi-machine power systems under various disturbances. It also investigated the performance of the proposed controller with variations in signal transmission delays. The results obtained from GOA optimized proposed controller are compared with those obtained from the differential evolution algorithm, genetic algorithm, and whale optimization algorithm. In this context, the proposed GOA optimized controller reduced the objective function value by 16.32%, 14.56%, and 13.72%, respectively, in the SMIB system and 1.41%, 9.98%, and 13.31%, respectively, for the multi-machine system compared with the recently published WOA, and the well-established GA and DE. Further, the proposed controller is found to be stable and effectively increases stability even under small disturbances.