DERIVATION OF SENSITIVITY-BASED OPTIMIZATION OF PID AND FUZZY REGULATOR IN HYBRID PV/ WIND SYSTEM THROUGH ADAPTIVE PARTICLE SWARM INTELLIGENCE

Abstract

This solution has been developed by the need to integrate photovoltaic (PV) and wind energy to create renewable energy system (HRESs) in place of managing variations and intermittency of renewable sources inherent in natural sources. However, even strong and effective energy control is difficult to optimize controller parameter towards because the system performance decreases significantly depending on the change of the parameters. This research gap is bridged by analytically carrying out a sensitivity analysis study on the tuning parameters of hybrid intelligent controllers such as Proportional-Integral-Derivative PID and Adaptive Particle Swarm Optimization APSO controllers. The effect of certain vital parameters on some key performance efficiency such as PID gains, APSO coefficients and fuzzy logic membership structures on some key performance efficiency parameters such as Rise Time, overshoot, settling time and steady state error were evaluated through a complete simulation utilizing MATLAB Simulink. The findings indicated that APSO optimization demonstrated significant improvements when compared to the conventional Genetic Algorithms (GA) based means because it recorded significant reductions in overshoot (66.67305 percent), settling time (60 percent), rise time (50 percent) and steady state error (58.33 percent). On the basis of these, an adaptive real time tuning system was designed that enhances system robustness in addition to responsiveness since an operating system operates in dynamic conditions. Important theoretical developments to real systems in this area Theoretical developments with important methodological implications on the design of efficient and reliable hybrid renewable energy systems.