AUIQ Technical Engineering Science

This study developed a microcontroller-based system which coordinates and monitors hybrid renewable energy sources within a microgrid, providing an uninterrupted power solution for off-grid areas. Site data for wind speed, solar radiation, and load demand were collected and averaged into hourly intervals across seasons. A key novelty of this work is an intelligent coordination approach based on Sequential Quadratic Programming (SQP) that simultaneously determines economically viable system sizes and optimizes real time energy flow for adaptive, reliable, and cost-effective control. Mathematical models and size optimization were developed for all system components. This study proposes an intelligent coordination and optimal sizing framework for an off-grid hybrid renewable energy system comprising 500kW of solar PV, a 400-kW wind turbine, pumped hydro storage, battery–inverter units, and a biogas generator. The system is designed to supply the electricity demand of an academic facility using only renewable sources with biogas backup. Simulation results show that renewable energy meets up to 96.4% of the total load during weekends in June and 51.4% during weekdays in December, while storage units supply between 20.1% and 44.0% of the demand, thereby smoothing the variability of wind and solar resources. The proposed coordination strategy achieves a 70% reduction in fuel consumption and guarantees a reliable supply with a loss of power supply probability of zero, corresponding to a reliability level above 99%. Among the candidate configurations evaluated, the PV/wind/pumped hydro/battery/biogas system emerges as the most cost-effective, yielding a net present cost of 3.09M$ and a levelized cost of energy of 0.027$/kWh. These results demonstrate that the optimized hybrid configuration provides a techno-economically viable pathway for fully renewable, reliable power supply in remote institutional applications.

Permanent Magnet Synchronous Motors (PMSMs) have been widely employed in numerous applications, on account of their high efficiency, compact structure, and high-performance torque control. Nevertheless, the dynamic and uncertain control PMSMs under the action of system perturbation and variation is a difficult problem in the field of traditional control law by using Proportional Integral Derivative controller (PID),Field oriented control (FOC) and Direct Torque Control (DTC) etc. In this article, we offer a critical survey of intelligent control methods such as Fuzzy Logic Control (FLC), Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), Evolutionary Algorithms (EAs) and discuss their advantages in accommodating nonlinearity, uncertainty of the system and real-time adaptability. By these methods, the performance and energy should be comparable/more optimal, and robust to the traditional setting. Moreover, the paper also discusses practical issues such as computation complexity, hardware complexity, and power consumption, advocating hybrid or simplified control models as possible solutions. Finally, the outlook for smart PMSM control is also presented, especially in EVs and renewable areas

This study presents an advanced performance analysis of a fuzzy-PID controller for Single-Phase Interior Permanent Magnet (IPM) motors using MATLAB/Simulink simulations. The fuzzy-PID controller dynamically adjusts its gains in real time via fuzzy logic, enhancing speed regulation and disturbance rejection while addressing the motor’s inherent nonlinearities and saliency effects. Performance comparisons with a conventional fixed-gain PID controller reveal that the fuzzy-PID achieves a 42% faster rise time, 68% lower overshoot, and reduced steady-state error (<0.8 rpm vs. ∼5.2 rpm for PID). Under ramp and sinusoidal inputs, the fuzzy-PID maintains lower tracking errors (∼7.5 rpm vs. ∼26 rpm for PID) and minimal phase lag (7° vs. 22° for PID). Robustness tests demonstrate superior disturbance rejection and faster recovery from load variations. The results confirm that fuzzy-PID control significantly improves transient response, accuracy, and adaptability for single-phase IPM motor drives, making it suitable for high-performance applications.