الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
In a power system, the load keeps changing which creates an imbalance between the power generated and consumed. This can cause the system frequency to deviate from its normal value, leading to unstable and oscillatory responses. To tackle this, it’s essential to have a control loop in place to stabilize the system frequency during load changes. In this context, a three-area system connected by tie lines is introduced in this thesis. The primary objective of the control strategy is to optimize frequency fluctuations and power deviations across the three areas and tie lines.
The system employs three controllers, namely Tilt-Integral-Derivative (TID), Fractional order Proportional-Integral-Derivative (FOPID), and Proportional-Integral-Derivative (PID) controllers, to achieve its objectives. These controllers are optimized using a new optimization algorithm, the Jellyfish Search (JS) Optimizer, which is inspired by the behavior of jellyfish in the ocean. The JS Optimizer is compared with other optimization techniques like the Grey Wolf Optimization (GWO) algorithm and Genetic Algorithm (GA) to validate its performance. Additionally, the system incorporates renewable energy sources such as wind energy and photovoltaic, based on real data. To tackle the issue of intermittent behavior in renewable sources, energy storage devices (ESDs) like superconducting magnetic energy storage (SMES), capacitor energy storage (CES), and battery energy storage (BES) are also integrated to improve the system’s performance.
A novel control approach, called Adaptive Super-Twisting Sliding Mode Control (ASTSMC), has been proposed to enhance the frequency performance of multi-pool systems. The effectiveness of this study was assessed by comparing it with three conventional controllers - Tilt-Integral-Derivative (TID), Proportional-Integral-Derivative (PID), and Fractional-order PID (FOPID). The parameters of these controllers were obtained using a physical meta-heuristic optimization technique called Transient Search Optimizer (TSO), which is inspired by the dynamic behavior of electrical circuits. The simulation results showed that ASTSMC outperformed the other controllers by managing the transient interval of the system response more efficiently. To validate TSO, it was compared with GA, and TSO was found to produce superior results. Furthermore, the study was extended to incorporate Renewable Energy Sources (RESs) such as photovoltaic and wind energy systems, and the ASTSMC was tested with both industrial and residential load models. Finally, energy storage devices such as batteries, SMES, and CES were utilized to mitigate the rapid fluctuations in the system response, and they were successful in significantly damping the system oscillations.