Improved Deep Neural Network (IDNN) with SMO Algorithm for Enhancement of Third Zone Distance Relay under Power Swing Condition

Cholleti Sriram, Jarupula Somlal, B. Srikanth Goud, Mohit Bajaj, Mohamed F. Elnaggar and Salah Kamel
Additional contact information
Cholleti Sriram: Department of Electrical and Electronics Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India
Jarupula Somlal: Department of Electrical and Electronics Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India
B. Srikanth Goud: Department of Electrical and Electronics Engineering, Anurag University, Venkatapur, Ghatkesar, Medchal, Telangana 500088, India
Mohit Bajaj: Department of Electrical and Electronics Engineering, National Institute of Technology Delhi, New Delhi 110040, India
Mohamed F. Elnaggar: Department of Electrical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia
Salah Kamel: Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt

Mathematics, 2022, vol. 10, issue 11, 1-19

Abstract: A zone 3 distance relay is utilized to provide remote backup protection in the event that the primary protection fails. However, under stressful situations such as severe loads, voltage, and transient instability, the danger of malfunction in distance relay is relatively high since it collapses the system’s stability and reliability. During maloperation, the relay does not function properly to operate the transmission line. To overcome this problem, an advanced power swing blocking scheme has been developed. An improved DNN-based power swing blocking system is proposed to avoid the maloperation of the distance relay and improve the system’s reliability. The current and voltage signal of the system is sensed, and the sensed data is fed into the Improved Discrete Wavelet Transform (IMDWT). The IMDWT generates the coefficient value of the sensed data and further computes the standard deviation (SD) from the coefficient, which is used to detect the condition of a system, such as normal or stressed. The SD value is given to the most valuable algorithm for the improved Deep Neural Network (IDNN). In the proposed work, the improved DNN operates in two modes, the first mode is RDL-1 (normal condition), and the second mode is RDL-2 (power swing condition). The performance of the IDNN is enhanced by using the threshold-based blocking approach. Based on the threshold value, the proposed method detects an appropriate condition of the system. The proposed method is implemented in the Western System Coordinating Council (WSCC) IEEE 9 bus system, and the results are validated in MATLAB/Simulink software. The overall accuracy of the proposed method is 97%. The proposed method provides rapid operation and detects the power swing condition to trip the distance relay.

Keywords: zone 3 distance relay; power swing; Improved Discrete Wavelet Transformation (IMDWT); Improved Deep Neural Network (IDNN) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/11/1944/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/11/1944/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:11:p:1944-:d:832500

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().