Advanced adaptive control of photovoltaic systems through LOA-tuned hybrid ANFIS–2DoF-FOPIDN

The manuscript proposes the modelling, design and performance assessment of a hybrid intelligent control architecture for the standalone solar photovoltaic (Solar PV) systems to achieve high-performance power conversion with enhanced efficiency and dynamic stability. The proposed system integrates a solar photovoltaic array with an Interleaved Super-Lift Luo (I-SLC) DC-DC converter and a H6 Transformerless Inverter (TI). On the DC side, a Lyrebird Optimizer Algorithm (LOA) tuned hybrid control approach is integrates the design flexibility of a Two-Degree-of-Freedom Fractional-Order PID controller with filter (2DoF-FOPIDN) and the adaptive learning capability of the Adaptive Neuro-Fuzzy Inference System (ANFIS), to improve the accuracy of the voltage regulation, enhance noise rejection and parameter tuning flexibility. On the other hand, during the AC conversion stage, the inverter is regulated by an LOA-assisted PI controller combined with Multi-Carrier Sinusoidal PWM (LOA-PI-MCSPWM), which ensures the stable output voltage with reduced harmonic distortion. The proposed scheme is executed using MATLAB/Simulink and evaluated through extensive transient and steady-state analyses, including variation in load. Comparative performance assessment against recently reported control methods demonstrates superior voltage regulation, faster settling characteristics, lower total harmonic distortion, and enhanced disturbance rejection. The effectiveness of the proposed approach is further established through real-time hardware-in-the-loop validation using the OPAL-RT 4510 platform. Not applicable.