Modeling and Control of Photovoltaic-Wind Hybrid

Abstract

As energy demands around the world increase, the need for renewable energy sources that will not harm the environment increases. Renewable energy, such as wind and solar energy, is desirable for power generation due to its unlimited existence and environmental friendly nature. However wind and solar sources are not reliable in terms of sustainability and power quality due to their inter- mittent nature. A management system is thus required for supplying the load power demand. This thesis presents a control strategy for power management in a standalone solar photovoltaic and wind hybrid power system based on arti- cial intelligence techniques. To ensure e cient optimization of sources, Adap- tive Neural Fuzzy Inference System (ANFIS) strategy is employed to achieve the Maximum Power Point (MPP) for photovoltaic (PV) panels and the Fuzzy Logic Control (FLC) strategy is used to achieve the MPP of wind turbine. Moreover, the FLC power management strategy is developed to manage the power ow to the system. The FLC chooses the optimal operating mode of power sources en- suring continuous supply of the load and maintaining the battery state of charge (SOC) at acceptable levels. The proposed system and its control strategy was as- sessed using a hybrid system comprising of PV panels, wind turbine and battery storage. Perturb and observe (P&O) MPP algorithm is used for a comparison with the proposed ANFIS MPPT system. From the simulation results based on the mathematical model of the system, the comparison of proposed MPPT with the classical P&O reveals the robustness of the proposed PV control system for solar irradiance and temperature changes. Moreover results also show that the proposed FLC Power management strategy for the hybrid system gives a greater reliability in terms of power generation and distribution compared to a stand alone system with single source. It provides e ective utilization of power sources and minimizes usage of the battery, hence improving its life. The whole system is analyzed through simulation in MATLAB / Simulink environment.