<![CDATA[In the operational state of an electrical power system, ensuring efficient utilization and high-quality power usage is essential. Various quality enhancement measures, such as linear and adaptive filters, are implemented to improve the current's quality. Additionally, power flow controllers are employed to mitigate losses and enhance fault tolerance. However, the escalating demand for power supply, driven by rapid industrial and urban growth, often exceeds the capacity of existing generation systems. To address this challenge, supplementary subunits are integrated into the power system. This proposal's main objective is to introduce a weight-defined parameter monitoring system for power scheduling within a multi-parameter monitoring framework. The aim is to enhance the conventional preference-based scheduler by incorporating intelligent control techniques, including Unified Power Quality Conditioner (UPQC) with the ANT-LION Optimization (ALO) algorithm, which will be compared to a Fuzzy Logic controller. UPQC plays a pivotal role in addressing power quality issues within the system, combining a shunt active power filter with an Artificial Neural Network (ANN) controlled by the ALO algorithm. Our research demonstrates the effectiveness of this proposed system, particularly in microgrid applications, with validation conducted using MATLAB/Simulink. ]]>
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