IRSA LAB

Wind Power

Wind Power With Fuzzy Control

A novel method is introduced for optimum energy harvest from wind farms. In the proposed method,  wind farm is modeled by fuzzy-logic and the model is updated using a combination of wind parameters history and wind’s spatial information. Utilizing this model, the parameters for the wind blowing through each turbine in the wind farm is estimated. To evaluate the performance of the proposed method two practical wind types are simulated. In the first scenario, the wind maintains low turbulence and its parameters change slowly while in the second scenario the wind demonstrates high turbulences and its parameters undergo sudden shifts. Simulation results for the proposed method are obtained in both scenarios. For the first scenario, the comparison reveals that the proposed method improves the accuracy of wind speed estimation and the monotonousness of the obtained electrical voltage by 5.3% and 0.52 volts respectively compared to existing methods. These improvements reach 17.1% and 12.7 volts in the presence of high turbulence winds in the second scenario. Based on these corroborating simulations, it is concluded that the proposed method provides a more accurate estimate of wind parameters for the wind blowing through the wind farm.

 

• Mojtaba Farzaneh - Armin Parsian Nezhad
• A complex and highly non-linear fluctuations and many factors including the additional loads and fluctuating parts and blades, etc will make disorder in the system which its control in this regard is of importance. In particular, this paper aims to improve Drive-train unit DFIG Generator model and controlling the rotor speed, the angle and reactive power consumption of a fuzzy controller design method which will be applied ANFIS. Finally, the results of simulations are presented
  

Nonlinear systems like wind turbines having different work areas by using linear controllers ratio would not give desirable responses. Bearing that against the small changes in the system, Controllers cannot be the correct control. So, one way to solve this issue might be the use of fuzzy Controllers. In the previous works for complex systems and non-linear has shown the amazing response. One of the methods in the use of fuzzy control, approximations Others controlled is in the form of fuzzy system that In addition its benefits of the controller. Characteristics of the fuzzy systems including issues regarding its robustness and its nonlinear behavior can be exploited. For jobs more can be combined with non-fuzzy control the method with fuzzy control Such as neural network control, etc. be named. Also, the application of two controllers: Rotor speed and steps angle in both areas seem s very useful in action and weight control separately in two controllers in order to reduce the number.

 

Software:

The Software Design with Delphi

 

 

Consultants
Professor Mohammad Bagher Menhaj
Dr. Seyed Vahab Shojaedini

 

Members Team:
Mojtaba Farzaneh
Armin Parsian Nezhad

 

 

Email: [email protected]

Tell: +98-912-733-6166

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