By Topic

Dynamic Load Modeling for Power System Based on GD-FNN

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Li Xiaofang ; Hunan Univ. of Electr. & Inf. Eng., Changsha, China ; Peng Minfang ; He Hao ; Liu Tao

Considering the characteristics of time-varying, diversity and randomness for electric power load, this paper puts forward a generalized dynamic fuzzy neural network (GD-FNN) load model to describe dynamic characteristics of electric power load. The learning algorithm takes fuzzy -completeness as a standard to adjust parameters, and the algorithm can make a comment on the fuzzy rules and the importance of input variables, ensuring that input variable width of every rule will be auto-adapted adjustment according to its contribution to the system, thus synchronous identification for the load model structure and parameters could be achieved. The simulation from a substation measurement data indicates that the generalized dynamic fuzzy neural network load model has a good fitting degree and strong generalization capability.

Published in:

Digital Manufacturing and Automation (ICDMA), 2012 Third International Conference on

Date of Conference:

July 31 2012-Aug. 2 2012