A robust transmission line fault classification scheme using class-dependent feature and 2-Tier multilayer perceptron network

• Published in: Electrical Engineering
• Main Author: Mahmud M.N.; Ibrahim M.N.; Osman M.K.; Hussain Z.
• Format: Article
• Language: English
• Published: Springer Verlag 2018
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017606956&doi=10.1007%2fs00202-017-0531-5&partnerID=40&md5=77689fc07e77d9d366cdb55d23190de9

Faults frequently occur in transmission lines and become a major issue in power system engineering. It is an unavoidable incident and leads to many problems such as failure of equipment, instability in power flow and economical losses. Protection relay is a device installed in transmission lines to perform detection, classification and estimation of fault. The task must be performed accurately and fast to isolate the faulted phase and protect the system from the harmful effects of the fault. Fault classification involves the process of identifying the type of fault in the transmission lines. The presence of ground fault in three-phase faults has caused difficulty in identifying the fault types. This is due to the input signals that only contain three-phase currents/voltages and influenced by noise. Recent studies show that ANNs are powerful tools for fault classification. However, most studies utilized single ANN to classify all the faults, even though not all fault classes are equally difficult. This paper proposes a robust fault classification scheme using wavelet transform (WT) and 2-Tier multilayer perceptron (MLP) network. The first-Tier MLP network (MLP 1) is used to classify the three-phase fault (A, B and C), and the second-Tier MLP network (MLP 2) is used to identify the presence of ground fault. A new WT-based feature that properly describe the presence of ground fault called class-dependent feature (CDF) is formulated. The CDF is determined from the correlation between the output of MLP 1, wavelet mean and energy features. The CDF is fed into the MLP 2 and used to determine the presence of ground fault. Comparison performance with different MLP network structures indicated that the proposed method showed good classification accuracy. The average accuracy of CDF and 2-Tier MLP network for three different datasets, ideal (no noise), 20 and 30 dB, shows the highest with 99.36% as compared to other structures. © 2017, Springer-Verlag Berlin Heidelberg.