AI-DRIVEN MODEL REFERENCE ADAPTIVE CONTROL FOR SMART ELECTRONIC LOAD CONTROLLER IN MICROHYDRO POWER PLANTS - Rahmad Syaifudin; Anang Sugeng Cahyono; Danang Wijanarko; Arya Wiratama; Muhammad Cahyo Bagaskoro (STKIP PGRI Tulungagung, 2026)
Stable regulation of output voltage and frequency in standalone microhydro power systems remains a significant challenge under varying load conditions. Traditional Electronic Load Controllers using fixed PID parameters often fail to respond effectively to nonlinear dynamics, load fluctuations, and environmental uncertainty. This work introduces an AI-based Model Reference Adaptive Control Electronic Load Controller designed for autonomous voltage regulation in microhydro plants. The controller continuously adjusts parameters in time by minimizing error between output and a predefined reference model. Performance is evaluated Simulink simulations under nominal load, sudden load increase, and sudden load decrease. Results indicate dynamic response with 0.18 s settling time, 4.2% overshoot, and 0.9 V steady-state error. Voltage recovery after disturbances occurs 0.14 s. Compared to PID-based controllers, improvements include reduced settling time, overshoot, steady-state error, and tracking error. demonstrates robustness and suitability for smart renewable energy systems.