Optimization of power coefficient (Cp) in variable low rated speed wind turbine using increamental Particle Swarm Optimization (IPSO)

Renewable Energy became a popular topic at the beginning of the third millennium in order to seek an alternative energy and it is expected to be environmental friendly. This is caused by the consumed daily energy like coal, petroleum, natural gas and others are no longer exist and cannot be renewed. Wind energy is one of the energy which is easy to use and can be obtained through wind energy conversion system or it is usually called by wind turbine. A wind turbine should have an equal or close power coefficient (CP) value to the maximum wind turbine standard value. CP is the determining factor for the advisability of wind turbines, because the greater value of CP the wind energy conversion will also be greater. IPSO is a combination of Particle Swarm Optimize (PSO) technique with Incremental Social Learning (ISL). The addition of the ISL algorithm allows this method to obtain global optimum value faster because ISL is a method of adding particle at specific time based on existing information. In this research PSO and IPSO will be compared in order to find an optimum CP from wind turbine prototype. Comparison result with mathematical approach is produced MSE = 0.0258for PSO and 0.0222for IPSO.