Modeling and Swarm Intelligence Based Control of Hybrid Wind-PV System for Grid Integration

With the degradation of fossil fuels, recent era witness the penetration of renewable energy sources like wind and solar energy into various electrical applications. Integration of these renewable energy sources is of prime importance as they possess zero carbon emission, environmental friendly and zero fuel cost. However, the unpredictability and unreliable nature of solar and wind motivates the combine utilization of these sources i.e. hybrid energy systems. These systems are more reliable and have better continuous production of electrical energy than using the sources individually. Combination of hybrid energy system into grid/standalone applications demands the use of power electronic interface and appropriate control strategy. In this context, this thesis aims at development of a hybrid Photovoltaic (PV)/wind energy based systems for grid connected application. PV and wind are hybridized on a DC side to avoid the synchronizing issues between the sources. However, the proposed hybrid system is integrated on distribution side of the grid with a DC/AC converter (inverter). Considering the essential need of synchronization, the control input i.e. pulses to the inverter are generated from a voltage and frequency controller i.e. Phase Lock Loop (PLL).The task of tuning the controller is formulated as an optimization problem and is solved using Particle Swarm Optimization (PSO) technique. The objective of the system is to meet the load demand and to manage the power generated from different sources at different operating conditions. Each module in the complete system is modeled on Matlab/Simulink platform. Also, the performance of the system is tested for additional utilization of battery charging.