Presently there is a growing energy demand usually covered by energy sources such as fossilrnfuel, coal and natural gas, which have been the basis for growth and development of thernpeople since the beginning of 20th century. Wind is an intermittent generation resource andrnweather changes can cause large and rapid changes in output, system operators will needrnaccurate and robust wind energy forecasting systems in the future.rnMain defects in wind turbine are the emergence of an unanticipated outcome which are notrnconsidered during the design of the blade. The turbine is generally described by wind speedrninputs and power output which represent the internal state of the wind turbine. Turbinerndisturbances can always exist in small or large scale depending on the environment of thernsystem. The disturbances may be results of wrong measurement (unnecessary inputrninsertion), or it can be the result of unnecessary (not optimal) power output, instrumentalrndefect.rnUsing a mathematical model of non-linear systems such as model to analyze differentrnpossible disturbances by wind speed on the wind turbine power output with one interval forrneach parent in a given time. In each generation, the fitness of every individual in the powerrnoutput is evaluated, multiple individuals are stochastically selected from the current powerrnoutput (based on their fitness), and modified (recombined and possibly randomly mutated) tornform a new power output. The new power output is then used in the next iteration of thernalgorithm.rnThe aim of this paper is to design a genetic algorithm based system state approximationrnmechanism for the wind turbine for which disturbances are assumed to be random defects,rnwhose values are in a known limited intervals.rnGenetic algorithm approach is a proficient approach for modeling the power systems thatrninspired their design and proposes to find out best solutions of the problems with anyrnmathematical equations.