Skip to content

dykesk/GeneratorSE

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DEPRECATED


THIS REPOSITORY IS DEPRECATED AND WILL BE ARCHIVED (READ-ONLY) IN NOVEMBER 2019.

WISDEM has moved to a single, integrated repository at https://github.com/wisdem/wisdem


GeneratorSE

GeneratorSE is a set of analytical tools for sizing variable speed wind turbine Generators. The analytical framework involves electromagnetic, structural, and basic thermal design that are integrated to provide the optimal generator design dimensions using conventional magnetic circuit laws.

The tool is structured on OpenMDAO and mainly considers available torque, mechanical power, normal and shear stresses, material properties, and costs to optimize designs of variable-speed wind turbine generators by satisfying specific design criteria.

Author: NREL WISDEM Team

Documentation

See local documentation in the docs-directory or access the online version at http://wisdem.github.io/GeneratorSE/

Installation

For detailed installation instructions of WISDEM modules see https://github.com/WISDEM/WISDEM or to install GeneratorSE by itself do:

$ python setup.py install

Run Unit Tests

To check if installation was successful try to import the package.

$ python
> import XXX

You may also run the unit tests which include functional and gradient tests. Analytic gradients are provided for variables only so warnings will appear for missing gradients on model input parameters; these can be ignored.

$ python src/test/test_GeneratorSE.py

For software issues please use https://github.com/WISDEM/GeneratorSE/issues. For functionality and theory related questions and comments please use the NWTC forum for Systems Engineering Software Questions.

About

DEPRECATED: A generator sizing tool for different types of generators

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 73.3%
  • MATLAB 26.7%