Sensitivity-Guided Optimization of PID and Fuzzy Controllers in Hybrid PV/Wind Systems Using Adaptive Particle Swarm Intelligence

Abstract

The need to combine photovoltaic (PV) and wind energy to form hybrid renewable energy systems (HRESs) to manage inherent variability and intermittency of renewable sources has led to development of this solution. Nevertheless, robust and efficient energy management is still challenging to optimize controller parameter for, as the performance of the system is highly sensitive to parameter variations. This gap is closed in this research through systematically conducting a sensitivity analysis on tuning parameters of hybrid intelligent controllers, including Proportional-Integral-Derivative (PID) and Adaptive Particle Swarm Optimization (APSO) controllers. The influence of some critical parameters, such as PID gains, APSO coefficients and fuzzy logic membership configurations on some critical performance indicators like Rise Time, overshoot, settling time and steady state error, were assessed by way of comprehensive simulations using MATLAB Simulink. Results showed that the APSO optimization considerably outperformed the traditional Genetic Algorithm (GA) methods, achieving great reductions in overshoot (66.67%), settling time (60%), rise time (50%) and steady state error (58.33%). Based on these, an adaptive real time tuning mechanism was developed that improves system robustness as well as responsiveness under dynamic operation conditions. The findings are important methodological contributions for design of efficient and reliable hybrid renewable energy systems from the theoretical advances to real systems in this field.