DESIGN AND ANALYSIS OF THE CHAMELEON SCHEDULING ALGORITHM FOR RECONFIGURABLE COMPUTING

Abstract

Conventional power generation is the source of widespread worry over the depletion of nonrenewable energy sources and the environmental difficulties that this would inevitably cause. As a direct consequence of this, an increasing number of people are gravitating toward renewable energy sources like as photovoltaic boards and wind generators. Wind power is being put to use for a vast array of applications, some of which include the charging of batteries, the pumping of water, the generation of electricity for homes, the heating of swimming pools, the operation of satellite power systems, and many more. However, despite the fact that they do not need any sort of upkeep and do not result in any kind of pollution, their installation costs are high in many different contexts. Wind energy is quickly becoming a more significant contributor to the total installed power capacity around the globe. In the field of wind energy, the PMSG-based wind turbine equipped with a variable-speed and variable-pitch control system are the most common type of wind power generator. This device is capable of operating either independently or while linked to an existing grid. It is necessary to have a complete understanding of the machine's modelling, control, dynamic, and steady-state analyses in order to obtain the maximum possible amount of power from the wind, researchers must make an accurate prediction of the machine's performance in either mode of operation. The complexity of the systems, the sensors required, the speed of combination, the cost, the breadth of effectiveness, the technology employed, and the popularity of the systems all vary. Multiple control computations are performed on the Wind Energy Conversion System (WECS) in order to produce maximum power in a variety of wind speed scenarios. This is done so that the system can respond appropriately. The purpose of this work is to suggest several methodologies for obtaining dynamic features from the WECS integrated grid. The first technique makes use of the Firefly Algorithm (FA) as well as the Artificial Neural Network (ANN) methods. Both are classification algorithms. By utilizing the most effective parameters for design, the design of the grid-integrated power system design was able to achieve a voltage THD of 11.47 percent.