By Topic

Synchronous machine steady-state stability analysis using an artificial neural network

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

2 Author(s)
Chao-Rong Chen ; Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan ; Hsu, Yuan-Yih

In the developed artificial neural network, those system variables which play an important role in steady-state stability, such as generator outputs and power system stabilizer parameters, are used as the inputs. The output of the neural net provides the information on steady-state stability. Once the connection weights of the neural network have been learned using a set of training data derived offline, the neural net can be applied to analyze the steady-state stability of the system in real-time situations where the operating conditions change with time. To demonstrate the effectiveness of the proposed neural net, steady-state stability analysis is performed on a synchronous generator connected to a large power system. It is found that the proposed neural net requires much less training time than the multilayer feedforward network with back-propagation-momentum learning algorithm. It is also concluded from test results that correct stability assessment can be achieved by the neural network

Published in:

Energy Conversion, IEEE Transactions on  (Volume:6 ,  Issue: 1 )