Fault location on a series‐compensated three‐terminal transmission line using deep neural networks. Issue 6 (1st September 2018)

• Record Type: Journal Article
• Authors: Mirzaei, Mahdi; Vahidi, Behrooz; Hosseinian, Seyed Hossein
• Is Part Of: IET science, measurement & technology. Volume 12:Issue 6(2018)
• Page Start: 746
• Page End: 754
• Publication Date: 2018-09-01
• Subjects: fault currents -- feature extraction -- discrete wavelet transforms -- power engineering computing -- neural nets -- power transmission faults -- power transmission lines -- fault location; series‐compensated three terminal transmission line -- deep neural network -- discrete wavelet transform -- DWT -- DNN -- fault location scheme -- SIMULINK model -- feature extraction -- synchronous measurement -- fault distance calculation -- intelligent network -- fault resistance -- fault current; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2018.0036
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : In this study, discrete wavelet transform (DWT) and deep neural network (DNN) are utilised for fault location in a series‐compensated three‐terminal transmission line. The series compensation causes challenges in fault location schemes of the three‐terminal transmission lines. The presented fault location method has been extensively tested using the SIMULINK model of a three‐terminal transmission line. Features extracted from synchronous measurements of fault currents at the three terminals using DWT are fed to the DNN. Faulted section determination and fault distance calculation are carried out using a single intelligent network simultaneously. Faulted section is determined with 100% accuracy, and the efficiency of algorithm is validated for symmetrical and unsymmetrical faults, and different values of fault resistance, inception angle, and location. The accuracy of the algorithm is acceptable for large fault resistances (above 100 Ω) and fault inception angles near zero. Total mean error for test data is 0.0458% which is much improved with respect to other similar works.