الفهرس 
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Abstract
Recent years have seen an increase in the integration of renewable energy sources, high voltage direct current, and energy storage devices, which has led to the transformation of the traditional power system into a hybrid power system. At the same time, the network that controls the power grid is becoming more flexible in order to better manage the fluctuating demand and supply. When there are sudden alterations to the electrical system, that include load fluctuations, loss of generators, or faults, these circumstances pose a challenge to establishing frequency stability. Because of this, load frequency control, also known as LFC, has moved away from traditional controllers and toward hybrid-intelligent-based controllers. Additionally, old optimization approaches have given way to more up-to-date metaheuristic optimizations in an effort to attain greater stability in hybrid power systems. Therefore, this thesis presents two methodologies to enhance frequency performance in power grids. The first control strategy relies on active power modulation, in which a fuzzy proportional-integral (PI) and fractional-order proportional-fractional filtered derivative action (FOPDFλ) regulator is proposed as a secondary controller to improve the frequency performance of a hybrid interconnected dual-area power grid with a unified power flow controller (UPFC) and superconducting magnetic energy storage (SMES) devices in both areas. In the proposed fuzzy PI-FOPDFλ controller, the benefits of fuzzy logic control (FLC), PI, and FOPD regulators, as well as the added flexibility to the derivative filter that is provided by employing fractional calculus, have all been incorporated. The Zebra Optimization Algorithm (ZOA) was utilized to fine-tune the suggested controller gains in the two-area hybrid power grids explored here. Reheat turbines and GRCs are represented in the system model. ZOA’s superiority is shown by comparing its results to those of the recently released optimizations Osprey Optimization Algorithm (OOA) and Jellyfish Search Optimizer (JSO). The ZOA-based fuzzy PI-FOPDFλ controller outperforms other ZOA-tuned fuzzy controllers like fuzzy PIDF and fuzzy PIDA. The power system was subjected to numerous Load patterns to improve system stability and validate the proposed fuzzy PI-FOPDFλ controller. These load patterns included step load fluctuation, pulse load fluctuation, and sinusoidal wave load fluctuation. Wind turbines and solar PV units have also been included into the power grid. In addition, a test case has been developed to demonstrate how the SMES and UPFC components boost system effectiveness overall. Despite the aforementioned difficulties, the suggested fuzzy PI-FOPDFλ controller, which is based on the ZOA, has been shown through MATLAB simulations to have the potential to boost system stability. On the other hand, the second control strategy is based on reactive power modulation, in which a novel optimized proportional-derivative with proportional-integral-derivative acceleration (PD-PIDA) driven static synchronous compensator (STATCOM) is proposed with the goal of improving the frequency performance of power systems through the modulation of reactive power. When there is a variation in load or when there is a failure of generation, the presented controller is able to maintain the frequency of the system within the allowable limits (±2% as per IEC 60034–1 standard). A recently developed metaheuristic optimization strategy known as the Artificial Rabbits Optimizer, or ARO, is used to do the fine-tuning the PD-PIDA controller’s gain settings. In addition to this, the functionality of the ARO-based PD-PIDA-controlled STATCOM is evaluated in the context of two IEEE standard systems: the two-area four-machine system (Kundur system) and the New England IEEE 39-bus system. The results show that the ARO-based PD-PIDA controller has a better frequency response than the Marine Predator Algorithm-optimized PIDA controller from the preceding literature. Furthermore, the suggested controller’s robustness is validated by merging case studies with wind generation. When the ARO-based PD-PIDA-controlled STATCOM was applied to any of the investigated situations, the frequency performance dramatically improved.