Abstract
High resistance fault poses an enormous challenge to the existing algorithms of fault detection and fault classification. In this paper, the standard deviation and accumulation method are employed to perform the fault detection and classification. It is primarily built in two stages. Firstly, the standard deviations for the measured current’s signals of the local and remote terminals is computed to extract the fault feature. Secondly, the cumulative approach is used to enlarge the fault feature to perform the high resistance fault. The proposed scheme is known as Standard Deviation Index (SDI), and it is obtained for the three phases and zero sequence. The proposed algorithm has been tested through different fault circumstances such as multiple faults locations, fault resistances, and fault inception time. Moreover, far-end faults with high-resistance, faults happened nearby the terminal, faults considering variable loading angle, sudden load change, different sampling frequency, bad signaling and a fault occurred in the presence of series compensation are also discussed. The results show that the proposed scheme performed remarkably well regarding the fault with resistance up to 1.5kΩ and can be detected within a millisecond after the fault inception. Additionally, the computational simplicity that characterizes the processes makes it more efficient and suitable for domain applications.
Highlights
The rapid growth of electric power systems over the past few decades has resulted in significant increase in the number of transmission lines
The output is named as standard deviation indices (SDI)
It was employed for obtaining the current signals that will be used to perform the proposed scheme tests
Summary
The rapid growth of electric power systems over the past few decades has resulted in significant increase in the number of transmission lines. Many researchers reported different techniques for fault detection and classification. It is characterized by its ability to track the transient phenomena that associates the faults [2] Artificial intelligence techniques such as Artificial Neural Network (ANN), Fuzzy, and Artificial Neural Network Fuzzy inference system (ANFIS), have an extensive usage in faults detection and classification process in power transmission line [3,4,5,6,7]. In the scheme [11] the dynamic features were extracted by stationary wavelet
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
