Wind Turbines 1 Abstract—The goal of this paper is to show the possibility of combining fault detection analysis, detection, modeling, and control of the doubly-fed induction generator (DFIG) wind turbine using intelligent control and diagnostic techniques. To enable online detection of problems inside the power electronics converter we apply the wave direct analysis method which enables a complete model for fault detection that includes the power electronic stage itself. A neural network system based on Hebbian networks is applied for fault classification with good detection results in simulation. For controlling the wind turbine a number different artificial intelligence techniques are presented including fuzzy logic and an adaptive fuzzy inference systems (ANFIS) which combines the characteristics of fuzzy logic and neural networks. A Grey predictor is also integrated in the control scheme for predicting the wind profile. The combined fault detection and control scheme are validated using simulation results. The software development and control platform is LabVIEW which is one of the most powerful tools for simulating and implementing industrial control systems. Keywords – Doubly Fed Induction generator, fault detection, LabVIEW, Fuzzy Logic, ANFIS and Grey predictor. I. INTRODUCTION Nowadays wind energy only supplies a fraction of the total power demand in the world but it is growing very fast while the cost per watt of the electricity produced falls proportionally. The progress of wind power in recent years has exceeded all the expectations, with Europe leading the global market [1,2], and global installed capacity increasing 14 fold from 2000 to 2011 and sufficient to cover 3% of the world's electricity demand. In the near future many countries around the world are likely to increase the level of wind energy generation. Wind turbines produce no CO 2 emissions and could help to reduce global greenhouse gas emissions. The cost of the electricity provided by wind energy facilities has been dropping since the 1980s, which makes wind power an interesting economical alternative energy source for developing regions of the world compared to fossil fuel based generation. The cost reduction is due to new technologies and higher production scales leading to larger and more reliable wind turbines. Hence, control systems have a key role in wind energy systems since the performance and reliability of the turbine can be significantly enhanced by intelligent control systems [6,10]. However, the inherent variability of the wind resource creates difficulties in forecasting the energy production of wind farms which makes management of the renewable resource more challenging [3]. This variability of the wind energy resource production is in contrast to the consistency of the power output from conventional fossil fuel based energy sources but can be mitigated through the use of intelligent control, energy forecasting, and smart grid technologies such as energy storage systems located by the wind farm. The efficiency of the wind turbine power generation can maximized if the rotational speed is such that the ratio between blades tip speed and wind speed, called tip speed ratio, is the optimal one at any time ( opt  ). One popular turbine topology in recent years has been the doubly-fed induction generator, which has the following features:  It generates power at constant frequency while operating at variable speeds.  The slip power is recovered and injected to grid by the power converter.  It can generate power with good efficiency over a reasonable speed range.  Since only a fraction of the power goes through the switched-mode power converter, its rating is significantly smaller than in variable speed / variable frequency schemes in which all of the turbine power is transferred through a back-to-back AC-to-DC converter and DC-to-AC inverter. Classical wind turbines are characterized by lower inertia than classical power plants. In the case of back-to-back converter/inverter topologies, common with permanent magnet synchronous machine (PMSM) type turbines, the Integrated Intelligent Control and Fault System for Wind Generators + Pedro Ponce, + Arturo Molina, * Brian MacCleery and # Kevin Wang + Instituto Tecnologico de Monterrey Campus Ciudad de México, * National Instruments Austin Texas, # ModelingTech Shanghai China Con formato: Español (México) Con formato: Español (México)