By Topic

A Fuzzy Neural Network Based Fault Detection Scheme for Synchronous Generator with Internal Fault

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
$33 $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)
Hongwei Fang ; Key Lab. of Power Syst. Simulation & Control, Tianjin Univ., Tianjin, China ; Changliang Xia

A fuzzy neural network (FNN) based inter-turn short circuit fault detection scheme for generator is proposed. The second harmonic magnitude of field current and the negative sequence components of voltages and currents are used as inputs for the FNN fault detector. The negative sequence voltage and current are obtained from the phase voltages and currents using the symmetrical component analysis method. And the second harmonic magnitude of field current is achieved by the FFT technique. The FNN fault detector with Gauss membership functions is trained off-line using the training data which comes from the Multi-Loop simulation program. The proposed fault detection scheme can perform the inter-turn short circuit fault detection, the fault type classification, and the fault location identification. Experimental results corroborate the effectiveness of the proposed scheme, which is implemented on a TI's DSP.

Published in:

Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on  (Volume:4 )

Date of Conference:

14-16 Aug. 2009