AI Summary & Key Takeaways
AI InsightThe proposed method not only provides a good classification performance for all types of faults, but also detects the faulty section at a high accuracy.
📌 3 Key Takeaways
Abstract
The fault detection process is very difficult in transmission lines with a fixed series capacitor because of the nonlinear behavior of protection device and series-parallel resonance. This paper proposes a new method based on S-transform (ST) and support vector machines (SVMs) for fault classification and identification of a faulty section in a transmission line with a fixed series capacitor placed at the middle of the line. In the proposed method, the fault detection process is carried out by using distinctive features of 3-line signals (line voltages and currents) and zero sequence current. The relevant features of these signals are obtained by using the ST. The obtained features are then used as input to multiple SVM classifiers and their outputs are combined for classifying the fault type and identifying the faulty section. Training and testing samples for the proposed method have been generated with different types of short-circuit faults and different combinations of system parameters in the MATLAB environment. The performance of the proposed method is investigated according to the accuracy of fault classification and faulty section identification. To evaluate the validity of this study, the proposed method is also compared to both ST--neural network and previous studies. The proposed method not only provides a good classification performance for all types of faults, but also detects the faulty section at a high accuracy.
Özet
The fault detection process is very difficult in transmission lines with a fixed series capacitor because of the nonlinear behavior of protection device and series-parallel resonance. This paper proposes a new method based on S-transform (ST) and support vector machines (SVMs) for fault classification and identification of a faulty section in a transmission line with a fixed series capacitor placed at the middle of the line. In the proposed method, the fault detection process is carried out by using distinctive features of 3-line signals (line voltages and currents) and zero sequence current. The relevant features of these signals are obtained by using the ST. The obtained features are then used as input to multiple SVM classifiers and their outputs are combined for classifying the fault type and identifying the faulty section. Training and testing samples for the proposed method have been generated with different types of short-circuit faults and different combinations of system parameters in the MATLAB environment. The performance of the proposed method is investigated according to the accuracy of fault classification and faulty section identification. To evaluate the validity of this study, the proposed method is also compared to both ST--neural network and previous studies. The proposed method not only provides a good classification performance for all types of faults, but also detects the faulty section at a high accuracy.