Energy Production

Wind Power

Wind Power

Harnessing one of the most abundant resources on earth requires the coordination of many disciplines. With MATLAB® and Simulink® products, power engineers can:

  • Analyze and predict wind conditions to optimize wind farm sites
  • Determine the proper voltage compensation to fuse wind farms into the electric grid
  • Monitor and process data to ensure wind turbine availability
  • Develop next-generation wind turbines

Analyze Data for Siting Wind Farms

Wind resource assessment is a critical tool for wind farm operators to maximize their large investments in land, infrastructure, and equipment when selecting a wind farm site. MATLAB data analysis and modeling tools enable engineers to analyze historical wind conditions, predict the energy generation potential of a site, and select and size appropriate wind turbine equipment for the site.

Engineers can use MATLAB to:

  • Import and preprocess data from wind data loggers, spreadsheets, and databases
  • Analyze historical wind velocity data using statistical methods
  • Develop accurate models to predict wind turbine energy output and extreme wind conditions
  • Share results by creating customized reports or Web applications
  • Use data analysis results as test cases for physical modeling of wind turbines

Optimize Wind Farm Integration Through Simulation

Effectively managing the operation and management of wind farm assets is critical to ensure economically efficient operation and quality of supply.

Wind farm system integrators and operators can use modeling and simulation to investigate reactive power management of the available assets on a wind farm. The goal of reactive power management is to regulate voltage at the grid point-of-connection, reduce voltage flicker, and manage the available reactive power capacity.

Model abstraction techniques, such as average-value power electronic converters and aggregated wind turbine representations, can be used to improve simulation speed. Parallel computing can be used to increase the speed of multiple scenario simulations.

Analyze Data to Improve Wind Farm Efficiency and Reliability

Improving the reliability of wind turbines and identifying failures before damage occurs is critical to both wind farm operators and turbine manufacturers.

With MATLAB, wind turbine manufacturers can trend and analyze process automation data to identify and prevent equipment problems and durability concerns, and investigate warranty issues.

Identifying equipment durability and warranty trends in large repositories of historical operations data can help improve turbine design reliability and minimize warranty and field service expenses. Turbine manufacturers can leverage analysis algorithms for turbine health monitoring using SCADA system data. With MATLAB deployment tools, custom analysis applications can be shared internally and with customers to enhance field monitoring and reduce maintenance costs.

Wind farm operators can optimize generation capacity and profits by using MATLAB to analyze relative costs and capacity of turbine assets for multiple factors including:

  • Peak selling times for power
  • Energy usage patterns
  • Forecasts of temperature, wind, and rain
  • Value of carbon credits
  • Transmission capacity on the grid

Use Model-Based Design to Design and Implement Wind Turbine Control Algorithms

Wind turbines are a complex interaction of mechanical and electrical systems designed to extract steady electrical power from a continuously varying source—the wind. Controlling the speed of the turbine involves continuous blade pitch control to produce maximum power and to maintain the turbine within proper operating limits under varying wind loads.

Model-Based Design helps wind turbine designers develop the necessary blade pitch control algorithms using desktop electromechanical simulation models to test the compensator strategy under operating conditions that cannot be easily verified in field conditions. Automatic C-code generation from the models lets engineers perform real-time testing of the control algorithms and deploy the control algorithms to the actual controller hardware.