Power Quality Enhancement and Optimization of Hybrid Renewable Energy System
Abstract
Wind and photovoltaic hybrid renewable energy resources for power generation due to their low environmental impacts have an attraction for lots of groups and countries, which will take part to adjust energy layout and secure environments. But there are many other factors such as non-linear behaviour, various objectives and various extreme values, need to determine. It is challenging to deal with them through orthodox techniques. Dynamic Voltage Restorer (DVR) is a method and electronic device to restore voltage during any disturbance in voltage supply. This research offers an Artificial Neural Network (ANN) based non-linear controller for built-up restoration capacity of DVR. ANN is a productive tool for a highly non-linear system. This paper further presents the optimization technique Particle Swarm Optimization (PSO) for tracking Maximum Power Point (MPPT) of wind/photovoltaic based hybrid power systems. Through this optimization technique can trace the wind speed and level of irradiation of solar to track the maximum point of power output for generation, due to its tracking ability generation from the hybrid system can get at their maximum rate which is economical too. In this work, we will compare the prediction with the help of PSO and Artificial Neural Network so that to show the more robustness and comparison between these two algorithms by implementing these two techniques on hybrid energies, i.e. photovoltaic and wind power energies.
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