Fuzzy logic control of MPPT controller for PV systems

This thesis presents a comparison between two methods to optimize the energy extraction in a photovoltaic (PV) power system. The maximum power of a PV module varies due to changing temperature, solar radiation, and load. To maximize efficiency, PV systems use a maximum power point tracker (MPPT) to constantly extract the highest power that can be produced by a solar panel and then deliver it to the load. The general structure of an MPPT system contains a DC-DC converter (an electronic device that converts a source of direct current DC from one voltage level to another) and a controller. The MPPT finds and maintains operations at the maximum power point using a tracking algorithm during variations in weather conditions. Many different algorithms of MPPT have been proposed and discussed in the literature, but most of these methods have disadvantages in terms of efficiency, accuracy, and flexibility. Because of the nonlinear behavior of PV module current-voltage characteristics and the nonlinearity of DC-DC converters due to switching, conventional controllers are unable to provide the best response, especially when dealing with wide parameter variations and line transients. The goal of this work is to design and implement a maximum power point tracker that uses a fuzzy logic control algorithm. Fuzzy logic naturally provides a superior controller for this type of nonlinear application. This method also benefits from the artificial intelligence approach for overcoming the complexity in modeling nonlinear systems. In order to succeed in this work, an MPPT system consisting of a PV module, a DC-DC converter, batteries, and a fuzzy logic controller is designed and simulated in Simulink. Analyses of buck, boost, and buck-boost converter characteristics are carried out to find the most suitable topology for the PV system used. An integrated model of the PV module with the identified converter and batteries is simulated in MATLAB to derive the expert knowledge needed to formulate and tune the fuzzy logic controller. The simulation results show that the fuzzy logic controller is able to obtain the desired outcomes and is ready to be applied to the hardware system. This entire research work aims to compare two types of controller-based MPPT techniques. Both MPPTs are based on the same topology of DC-DC converter and are applied with the same PV system specifications. That is, one of the MPPTs was kept at its original specifications and the other one was modified by changing the internal PIC 16F684 controller with an external Arduino Uno controller. Based on a MATLAB fuzzy logic design, the Arduino code was programmed and uploaded into an Arduino board by using Arduino software (IDE). The proposed method illustrates that the performance of MPPT is improved in terms of oscillations about the maximum power point, speed, and sensitivity to parameter variation. The results indicate that a significant amount of extra power can be extracted from a photovoltaic module by using a fuzzy logic-based maximum power point tracker in comparison with a PIC 16F684 controller-based maximum power tracker. Moreover, this gives improved efficiency for the operation of a PV power system, since batteries can be sufficiently charged and used during periods of low solar radiation