An IoT-Based Experimental Framework for Disturbance-Aware Analysis of MPPT Dynamics in Photovoltaic Systems

Under varying load, temperature and irradiance conditions, PV systems should be able to adapt to the nonlinear behavior and function with maximum power point tracking (MPPT). In most current works on MPPT, the efficiency in the steady state is the main concern, or a simulation approach is used to validate the results, few studies have explored the dynamic behavior and disturbance sensitivity under real operating conditions. A low-cost stand-alone embedded Internet of Things (IoT) based PV system is developed to test the performance of MPPT in a disturbance environment and an experimental framework is presented. The study does not propose a new MPPT algorithm, but looks at the MPPT system level performance of a standard perturb and observe (P&O) MPPT method running on a NodeMCU ESP8266 platform with integrated real-time control, DC–DC conversion, sensing, and IoT monitoring. The dynamic performance metrics are determined from the experimental data using MATLAB. The results indicate that a 40% reduction of irradiance will result in 21% increase in tracking time and steady state ripple will increase by 17 % while variations in temperature between 25 °C to 45 °C will result in less than 13 % change in tracking time. The convergence time is less than 20%, for 50% load variation, and the robustness is good. Further, a smaller perturbation step size will reduce the convergence time by up to 54%, with nearly a doubling in steady-state ripple, meaning there is a definite tradeoff between these. The study connects the dots between idealized MPPT analysis and actual PV system behavior in embedded systems when subject to disturbances in practice.