MODELING AND SIMULATION OF HIGH EFFICIENCY GRID-CONNECTED PV SYSTEM

Abstract

Modeling and simulation of a grid-connected photovoltaic system is considered. This work presents a fuzzy logic control-based maximum power point tracking approach to enhance the efficiency and robustness of the solar photovoltaic (PV) power generation, and establishes a dynamic model of a grid-connected PV system by Matlab/Simulink environment which reflects the characteristics of the system accurately. A grid-connected PV system includes a PV array, a DC/DC converter, a fuzzy logic control-based MPPT, an inverter, an LC filter, a P-Q-based inverter control, a distribution network. The FLC consists of three parts: a) fuzzification of input variables, which transforms a given set of numerical inputs (measured or computed voltage and current of PV array) into a fuzzy linguistic set of composed variables of fuzzy subsets called also membership functions: b) inference fuzzy rules which contains a set of fuzzy rules in a linguistic form, as well as a database which is a collection of expert control knowledge, allowing to  achieve  the fuzzy control objectives. This control rules base can be set up using IF-THEN rules based on expert experience and/or engineering knowledge, and learning the fuzzy rule-based system which has learning capabilities: c) defuzzification of the inference engine, which evaluates the rules based on a set of control actions for a given fuzzy inputs set. This operation converts the inferred fuzzy control action into a numerical value at the output by forming a “union” of outputs resulting from each rule. The center of the area (COA) algorithm is used for the defuzzification of the output control parameter.

The improved fuzzy logic control-based MPPT and the P and Q-based inverter control scheme provides a closed loop active and reactive power control and accurate synchronization to the grid. Simulation results presented here validate the component models and the chosen control schemes.