Article citationsMore>>

Zhang, X.Q., Liang, J. and Zhang, X. (2013). Combined Model for Ultra Short-term Wind Power Prediction Based on Sample Entropy and Extreme Learning Machine. Proceedings of the CSEE, 33, 33-40.

has been cited by the following article:

• TITLE: New Principle of Busbar Protection Based on Active Power and Extreme Learning Machine

  AUTHORS: Syed Hassan Lal Gilani, Xingxing Dong, Haiyan Xu

  KEYWORDS: Busbar Protection, Extreme Learning Machine, Active Power, S Transform, Fault Identification

  JOURNAL NAME: Open Access Library Journal, Vol.7 No.5, May 26, 2020

  ABSTRACT: In order to improve the reliability of busbar protection, a new fast busbar protection algorithm based on active power and extreme learning machine is proposed. By performing S-transformation on the fault voltage and current traveling wave, the active power amplitude within 0.1 ms after the fault is obtained. Simulate different fault types in the busbar area and build a bus fault feature vector sample set. The intelligent model of fault learning of extreme learning machine is established, and the sample set is used for training and testing to realize bus fault area identification. The simulation results show that the proposed busbar protection method can identify faults in the busbar area sensitively and reliably.