Multi extreme learning machine approach for fault location in multi-terminal high-voltage direct current systems. (September 2019)

• Record Type: Journal Article
• Authors: Hadaeghi, Arsalan; Samet, Haidar; Ghanbari, Teymoor
• Is Part Of: Computers & electrical engineering. Volume 78(2019)
• Page Start: 313
• Page End: 327
• Publication Date: 2019-09
• Subjects: HVDC -- Fault location -- Extreme learning machine -- ELM -- Artificial neural network -- ANN -- Support vector regression -- SVR -- Wavelet transform -- S transform; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.07.022
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: A method based on extreme learning machine (ELM) is suggested to locate faults in multi-terminal high-voltage direct current systems. S-transform and wavelet transform are used for extraction of the features used for the learning. The accuracy of the technique for various types of input signals and different lengths of the analyzed window is investigated. Two different approaches are considered for employing the ELM in this application. In the first approach, an ELM is used for total length of the line. In the second one, a multi-ELM technique is applied to different sections of the transmission line. In this approach, one ELM is considered for each of the divided sections. It is proved that the performance of the method is improved by the multi-ELM approach in comparison with the single ELM one. The performance of the ELM approach is compared with the artificial neural network and support vector regression techniques.