Modeling and Performance Investigation of GEOA-Based H-ANFIS-2DoF-FOPIDN Controller for Photovoltaic Systems
Abstract
This manuscript represents the modelling and performance assessment of a smart control architecture which is established to enhance the dynamic response, efficiency, and robustness of a standalone solar photovoltaic (PV) system. The proposed framework employs a PV array along with an Interleaved Superlift Luo (I-SLC) DC-DC step-up converter and a transformer-less H6 inverter to accomplish high quality power conversion across fluctuating operating conditions. On the DC side, a hybrid control approach is implemented by collaboratively combining the adaptive learning ability of an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) structure, the design elasticity of a Two Degree of Freedom Fractional Order PID controller with Filter (2DoF-FOPIDN), and the global search efficacy of the Golden Eagle Optimizer Algorithm (GEOA), enabling accurate voltage regulation with enhanced noise protection and parameter flexibility. On the other hand, the AC conversion stage, the control signals for the inverter are generated using a GEO-PI controller in combination with Multi-Carrier Sinusoidal Pulse Width Modulation (GEO-PI-MCSPWM), ensuring effective harmonic suppression and stable output voltage regulation. The proposed system is modelled and analysed using the MATLAB/Simulink to examine both transient and steady-state characteristics. Controller robustness is further scrutinized under dynamic operating circumstances and also step load variations. A comprehensive comparative assessment in contradiction of numerous contemporary control techniques determines substantial enhancements in voltage regulation accuracy as well as reduction in harmonic, settling time, and better disturbance rejection capability. Furthermore, the proposed model is also authenticated through implementation of real-time hardware-in-loop using the OPAL-RT 4510 platform to affirm the efficacy and practical suitability for the development of the next generation intelligent PV systems.