During the last decade, renewable energies have taken on a great importance in the development of some countries. Developing this energy is increasingly the axis explored by the scientific community. The energy can be extracted from many sources, to cite fossil, solar and wind [1].Wind turbine technology has developed rapidly in recent years and Europe is at the hub of this high-tech industry. Wind turbines are becoming more powerful, with the latest turbine models having larger blade lengths which can utilize more wind and therefore 66 produce more electricity, bringing down the cost of renewable energy generation. Wind turbines produce electricity by using the natural power of the wind to drive a generator. The wind is a clean and sustainable fuel source, it does not create emissions and it will never run out as it is constantly replenished by energy from the sun. In many ways, wind turbines are the natural evolution of traditional windmills, but now typically have three blades, which rotate around a horizontal hub at the top of a steel tower. Most wind turbines start generating electricity at wind speeds of around 3-4 meters per second (m/s), (8 miles per hour); generate maximum ‘rated’ power at around 15 m/s (30mph); and shut down to prevent storm damage at 25 m/s or above (50mph)[2].The main challenge encountered on wind energy is to be able to extract a maximum of power at each instant of the operating cycle of a turbine. There are several MPPT methods to extract the maximum of wind power, such as PI control [21], linear quadratic Gaussian (LQG) control [22], optimal control [23], sliding mode control (SMC) [24], predictive control [25], fuzzy control [26], combination of fuzzy ANN and PSO algorithm [27], harmony search algorithm [28], , perturbation and observation (P&O) or hill climbing searching (HCS) [31], wind speed measurement (WSM), tip speed ratio (TSR) control and power signal feedback (PSF) [29], [30]. In the P&O method, the rotor speed is perturbed by a small step, and then the power output is observed to adjust the next perturbation on the rotor speed [3].With power signal feedback (PSF) the reference optimum power curve of the wind turbine should be obtained first from the experimental results [4].The perturbation and observation (P&O), or hill-climb searching (HCS) methods are a mathematical optimization technique used to search for the local optimum point of a given function [4].Although the methods show good performance the research has never ceased to further improve the power rates extracted. In this article we will optimize and maximize the extracted power rate using an Ant colony optimization method. Ant colony optimization is based on cooperative behavior of real ant colonies, which are able to find the shortest path from their nest to a food source[5].This report deals the tuning of the control system for the classical MPPT of variable speed wind turbine. Where: PI-regulators are calculated with classical methods regulating rotor speed with ant colony algorithm PI-ACO. The disadvantage of the classical method is the obligation to calculate the machine parameters before make the control but, in this method we have no need to know the machine parameters, this represent a great advantage of resolution.