Hybrid ANN-INC MPPT Integrated with a High-Efficiency Boost VMS Converter for Photovoltaic Systems

The primary aim of this research is to present a hybrid maximum power point tracking (MPPT) algorithm that overcomes oscillation, slow response and high losses faced by conventional MPPT algorithms. The proposed hybrid technique combines artificial neural network (ANN) and incremental conductance (INC) MPPT. It intelligently switches between ANN and INC algorithms according irradiance level. At high irradiance which typically occurs under clear sky conditions, ANN is applied to achieve rapid and predictive tracking. For reduced irradiance levels below 400 W/m² that represents cloudy weather, INC is activated for stable performance. Besides, the hybrid ANN-INC MPPT is paired with an advanced boost voltage multiplier stage (VMS) converter to obtain a lower duty cycle and a higher voltage gain and consequently reducing switching and conduction losses during conversion. The entire system is simulated in MATLAB/ Simulink to investigate its performance under varying irradiance and load conditions. The results demonstrate the proposed system superior performance across all evaluated scenarios. Under high irradiance (1000 W/m²), the system reached a highest power of 398.283 W within a response time of 1.056 seconds and achieved a peak overall efficiency of 93.911%. Furthermore, under moderate irradiance (400 W/m²), it maintained the highest output power of 152.35 W and a peak overall efficiency of 95%. Under dynamic irradiance conditions, the system sustained a steady power of 155.524 W at the low irradiance plateau demonstrating stability and recovery. The synergistic operation of the presented hybrid ANN-INC MPPT with the low-loss boost VMS converter is confirmed as a significant breakthrough. Its high power and efficiency as well as smooth voltage and current signals make it effective for practical applications where finding true peak fast and accurately is a major challenge.