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Fault Detection on a 132kV Transmission Line Using Artificial Neural Network


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DOI: https://doi.org/10.15866/iree.v14i3.17054

Abstract


This paper utilises an artificial neural network (ANN) technique for fault detection on a 132kV transmission line system. A Feed-forward network with backpropagation algorithm was used in the fault recognition process for the Uyo to Eket 132kV transmission line on the Nigerian Power System under review. Due to a dearth in real-time data from the transmission line system under review, faults values (current and voltage) were generated using power system computer-aided design (PSCAD)/electromagnetic transient design and control (EMTDC) software model of the transmission line, with variations in fault resistance, distance and inception angle. The generated fault and no-fault data set were extracted using MATLAB software. The neural network toolbox in MATLAB was utilised for training the ANN-based relay system to detect faults on the three-phase transmission line system. The results obtained showed that the trained network with hidden layers architecture [15 15 10 5] has the best performance with mean square error (MSE) value of 0.000145 and correlation coefficients for validation and testing of 0.99998 and 0.99953 respectively. These results justify the reliability and accuracy of ANN-based protection relays for transmission lines fault detection.
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Keywords


Artificial Neural Network; Fault Detection; Transmission Lines; PSCAD/EMTDC

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