This study presents the implementation of Artificial Bee Colony (ABC) optimization in an island hybrid power system model using fuzzy logic-based load frequency control. The Island Hybrid Power System considered in this study consisted of various generation units and an energy storage system. The optimized control parameters of PID using ABC were used in an intelligent fuzzy logic controller. The profiles (power & Frequency) of isolated hybrid power system were improved using a Super Conducting Magnetic Energy Storage (SMES) System. Individual controllers were used for wind turbine and diesel generators to control the power output for balancing the demand (frequency change control). Comparative analysis of power and frequency with the help of various classical and intelligent control configurations is presented. The outcome of the study shows that a minimum deviation in frequency and power is obtained through the proposed Intelligent Fuzzy Control approach for the considered isolated power system model.

This study focuses on the design, development, and performance evaluation of the dual-axis solar tracker (DaST) system to maximize solar energy harvesting. The proposed system rotates solar panels along both horizontal and vertical axes, dynamically adjusting their orientation to follow the sun’s trajectory using a NodeMCU ESP8266 microcontroller. A novel Internet of Things (IoT) framework enables real-time system monitoring and performance optimization. A fully functional prototype of the IoT-enabled solar tracking system (STS) has been developed and rigorously tested to validate its performance and effectiveness in real-world applications. This research develops a transfer function model of the DaST to analyze the system dynamics under different control schemes. The stability and robustness of the DaST system with uncertainty, including the proportional-integral-derivative (PID) controller, are evaluated through performance degradation curves. The coefficients of the PID controller are optimized using the red colobuses monkey optimization (RCMO) algorithm. Finally, a comparative analysis was conducted to assess the performance of the DaST system with the RCMO Algorithm (RCMOA)-based PID controller against the firefly algorithm (FA), cuckoo search algorithm (CSA), and conventional PID controllers. The results indicate that the RCMO algorithm exhibits better stability and performance across all uncertainty levels compared to the other methods.