| Abstract: |
| As it is crucial to protect the transmission line from inevitable faults consequences, intelligent scheme must be
employed for immediate fault detection and classification. The application of Artificial Neural Network (ANN) to
detect the fault, identify it’s section, and classify the fault on transmission lines with improved zone reach setting
is presented in this article. The fundamental voltage and current magnitudes obtained through Discrete Fourier
Transform (DFT) are specified as the inputs to the ANN. The relay is placed at section-2 which is the prime section to
be protected. The ANN was trained and tested using diverse fault datasets; obtained from the simulation of different
fault scenarios like different types of fault at varying fault inception angles, fault locations and fault resistances in a
400 kV, 216 km power transmission network of CSEB between Korba-Bhilai of Chhattisgarh state using MATLAB. The
simulation outcomes illustrated that the entire shunt faults including forward and reverse fault, it’s section and phase
can be accurately identified within a half cycle time. The advantage of this scheme is to provide a major protection up
to 99.5% of total line length using single end data and furthermore backup protection to the forward and reverse line
sections. This routine protection system is properly discriminatory, rapid, robust, enormously reliable and incredibly
responsive to isolate targeted fault. |
| Key words: Artificial neural network, Fault classification, Fault detection, Fault direction estimation, Section identification |
| DOI：10.1186/s41601-016-0029-6 |
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| Fund: |
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