Model predictive control method to achieve maximum power point tracking without additional sensors in stand-alone renewable energy systems

Abstract

Maximum Power Point Tracking (MPPT) has an important role in studying and implementing photovoltaic systems. Different solutions have been recently proposed in numerous papers. Predictive control is simple in both method and implementation aspects. The main purpose of this control is to improve the maximum power point tracking using predictive error in Perturb and Observe method step. The current is predicted using the system model and the optimal operating point is determined via a certain cost function. By using the predictive equations, the additional sensors are removed. In MPC, the system equations must be ideally formulated. It means that if the system equations are written according to the real, actual conditions, the formulations become very complicated and take up a large amount of microprocessor time and memory. Another disadvantage of MPC is that the actual values of the system elements either are not available or can change over time. In this article, a Cuk converter is analyzed using the predictive control and Fuzzy Model Predictive Control (FMPC) method to achieve MPPT in photovoltaic systems. The Cuk converter has fewer active elements compared to ultra-step-up boost converter and the extra sensors are eliminated. It is shown that the predictive control method in ideal system has a better performance in both faster dynamic and steady state response under rapidly changing atmospheric conditions. The Predictive Control Method is compared with Perturb and Observe and Fuzzy Model Predictive Control via simulation results using Matlab and experimental results.