Abstract
In this paper, we have introduced a novel fault detection and classification technique for high-voltage DC transmission lines using K-nearest neighbours. The algorithm makes use of rectifier end AC RMS voltage, DC line voltage and current measured at both poles. These signals are generated using PSCAD/EMTDC and are further analysed and processed using MATLAB. For fault detection, the signals are continuously monitored to identify the instant of occurrence of fault, whereas for fault classification, the standard deviations of the data over a half cycle (with respect to AC signal) before and after the fault inception instant are evaluated. The algorithm hence makes use of a single-end data only, sampled at 1 kHz. The technique has proven to be 100% accurate and is hence reliable.
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Johnson, J.M., Yadav, A. (2018). Fault Detection and Classification Technique for HVDC Transmission Lines Using KNN. In: Mishra, D., Nayak, M., Joshi, A. (eds) Information and Communication Technology for Sustainable Development. Lecture Notes in Networks and Systems, vol 10. Springer, Singapore. https://doi.org/10.1007/978-981-10-3920-1_25
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DOI: https://doi.org/10.1007/978-981-10-3920-1_25
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