Abstract
In this paper, a new BP neural network has been built with GA algorithm, which possesses high effectivity, parallel processing ability and global search feature, in order to overcome the original shortcomings such as the low convergence rate and the confusion of local minimum points. Ten parameters of partial discharge characteristics are obtained as input of BPNN to identify four typical insulation defect physical models designed in this paper for the purpose of measuring the recognition accuracy of the improved BPNN. Experiments show that the improved BPNN has a better performance of convergence speed and recognition accuracy than that of adaptive momentum BP neural network.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
