Hybrid CNN-LSTM approaches for identification of type and locations of transmission line faults. (February 2022)

• Record Type: Journal Article
• Authors: Moradzadeh, Arash; Teimourzadeh, Hamid; Mohammadi-Ivatloo, Behnam; Pourhossein, Kazem
• Is Part Of: International journal of electrical power & energy systems. Volume 135(2022)
• Publication Date: 2022-02
• Subjects: Transmission Line -- Fault detection -- Frequency response analysis (FRA) -- Convolutional neural network (CNN) -- Long short term memory (LSTM) -- Convolutional-LSTM (C-LSTM); Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.107563
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Frequency response analysis (FRA) is used to fault identification considering different fault impedance, type and location. Deep learning applications CNN, LSTM, and C-LSTM are utilized for interpretation of the results of FRA. Abstract: Timely and accurate detection of transmission line faults is one of the most important issues in the reliability of the power systems. In this paper, in order to assess the effects of impedance and location of the fault in identifying and classifying it, the frequency response analysis (FRA) method is utilized. This method clearly shows the smallest effects of the faults on voltage and current signals in the frequency domain. Interpretation of the results associated with the FRA procedure is considered a weakness of this method. To overcome this issue and accurately categorize types and locations of various transmission lines faults such as asymmetric faults and symmetric faults, machine learning, and deep learning applications called support vector machine (SVM), decision tree (DT), k -Nearest Neighbors ( k -NN), convolutional neural network (CNN), long short term memory (LSTM), and a hybrid model of convolutional LSTM (C-LSTM) are utilized. Introduced faults are applied with various impedances in 6 segments of an IEEE standard transmission line system. Then, the frequency response curves (FRCs) for them are computed and selected as input datasets for the suggested networks. After categorizing the types and locations of faults, the results for each network are analyzed via different statistical performance evaluation metrics. Finally, in order to early detection of faults, the new high impedance faults (7000 and 9000 O) are applied based on the previous routine in the transmission line. At this stage, evaluations demonstrate the capability of the C-LSTM followed by SVM, DT, k -NN, CNN, and LSTM in categorizing the type and location of transmission line faults.