Transmission Line Protection Using High-Speed Decision Tree and Artificial Neural Network: A Hardware Co-simulation Approach

In this paper, a protection scheme of 500 kV, 50 Hz, and 220 km double end transmission system based on wavelet transform (WT), high-speed decision tree (DT), and artificial neural network (ANN) are proposed. The scheme is implemented using a microcontroller co-simulated with MATLAB/Simulink and it...

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Bibliographic Details
Published in:	Electric power components and systems Vol. 49; no. 13-14; pp. 1181 - 1200
Main Authors:	Fayyad, Amr Ahmed, Abdel-Gawad, Amal Farouk, Alenany, Ahmed Mahmoud, Saleh, Saber Mohamed
Format:	Journal Article
Language:	English
Published:	Philadelphia Taylor & Francis 11-05-2022 Taylor & Francis Ltd
Subjects:	artificial neural network Artificial neural networks Classification Classifiers Comparative studies Data processing decision tree Decision trees fault classification fault location fault zone identification Faults Hardware High speed Matlab microcontroller Microcontrollers Neural networks Real time Simulation transmission line Transmission lines Wavelet transforms
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Summary:	In this paper, a protection scheme of 500 kV, 50 Hz, and 220 km double end transmission system based on wavelet transform (WT), high-speed decision tree (DT), and artificial neural network (ANN) are proposed. The scheme is implemented using a microcontroller co-simulated with MATLAB/Simulink and it is validated in real-time. The proposed scheme performs fault classification, location estimation, and fault zone identification using both DT and ANN in a comparative study. The scheme calculates the wavelet transform approximation coefficients of the current and voltage signals and then sends its energy to a specific decision tree/neural network to recognize the fault type, its zone, and its location. It was found that the accuracy of the decision tree classifier outperforms the ANN classifier, in addition, achieves a significantly higher classification speed of about 0.4 msec inference time and it is easier to be applied in real-time. In contrast, the neural network is more accurate in calculating fault zone and locations. DT and ANN are trained and tested using different sets of data obtained from simulated faults that differ in type, resistance, and location. Modeling fault scenarios and data processing are carried out using the MATLAB/Simulink software package. The hardware implementation uses an 8-bit ATmega microcontroller that is interfaced with the simulated model using MATLAB support package for Arduino. Simulation results confirm that the proposed scheme using DT for fault classification and ANN for zone and location estimation is accurate and reliable even with varying fault resistance, type, and distance, and is therefore applicable in practice on a digital platform.
ISSN:	1532-5008 1532-5016
DOI:	10.1080/15325008.2022.2050446