A Robust Hybrid InC-FLC Maximum Power Point Tracking Method with Adaptive Step-Size and Noise Mitigation for PV Systems Under Dynamic Conditions

Andrew Habila John, Agbon E. E, Sagir M. Garba, Bello Usman Abdullahi, Aminu Chiroma Muhammad, Ahine O. Itamah

Abstract


This paper presents a comprehensive review and comparative analysis of hybrid Fuzzy Logic Control (FLC) based Maximum Power Point Tracking (MPPT) algorithms for photovoltaic (PV) systems, with particular focus on a novel hybrid Incremental Conductance-Fuzzy Logic Control (InC-FLC) approach. Photovoltaic systems face significant challenges in achieving optimal power extraction due to their nonlinear electrical characteristics and sensitivity to varying environmental conditions, including fluctuating irradiance, temperature changes, and partial shading. Conventional MPPT algorithms such as Perturb and Observe (P&O) and Incremental Conductance (InC), while widely used due to their simplicity, suffer from inherent limitations including steady-state oscillations around the Maximum Power Point (MPP), slow convergence under rapidly changing conditions, and compromised efficiency due to fixed step-size perturbation strategies. Simulation results under dynamic conditions, including abrupt irradiance changes at 0.2-second intervals ranging from 200 to 1000 W/m² and sudden load variations from 50Ω to 20Ω and 35Ω, demonstrate that the proposed algorithm achieves superior performance across all evaluated metrics. The proposed method attains an average MPPT efficiency of 97.7%, representing a 2.29% improvement over the conventional P&O algorithm (95.41%) and a 2.10% improvement over the InC algorithm (95.60%). The convergence time averages 53.5 milliseconds, which is 11.5 ms (17.7%) faster than P&O and 6.5 ms (10.8%) faster than InC. Most significantly, the proposed algorithm reduces steady-state oscillation amplitude to just 3.8 W, representing an 87% reduction compared to P&O (28.5 W) and a 75% reduction compared to InC (15.2 W). The root mean square error (RMSE) of 8.6 is the lowest among all algorithms evaluated, and the RMS percentage of 97.8% is the highest, indicating superior tracking accuracy and output stability. Under dynamic load variation tests at 1000 W/m² irradiance, the proposed algorithm achieves an RMSE of 25.15 and an RMS percentage of 97.92%, outperforming the InC algorithm (RMSE: 27.11, RMS%: 97.14%) and existing hybrid methods. Statistical analysis across irradiance levels from 75% to 100% reveals that the proposed method maintains tighter performance distribution with reduced variability, ensuring consistent and predictable operation. While the proposed algorithm exhibits minor limitations, including a slight delay of less than 5 milliseconds under sudden load variations and sensitivity to measurement instrument precision, these are substantially outweighed by its performance advantages. The review concludes that the choice of input variables and fuzzy rule base design critically impacts MPPT performance, and the proposed hybrid InC-FLC algorithm using SInC and CSI input variables offers a robust, efficient, and adaptive solution for maximizing power extraction from PV systems operating under diverse and dynamic conditions, making it highly suitable for real-world photovoltaic applications including grid-connected systems, standalone power supplies, and hybrid renewable energy installations. 


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