Fault Location Estimation in LCC HVDC Transmission Lines Using k-Nearest Neighbors

Finding fault location in HVDC transmission is essential to minimize the downtime. k-Nearest Network or k-NN has been widely studied as fault detection and location in HVAC transmission system. However, only a few works have been reported using k-NN for fault detection and location in HVDC transmission system. In this work, a CIGRE benchmark model of bipolar ± 500 kV HVDC system is constructed in PSCAD/EMTDC to evaluate fault condition. Fault signals are extracted using current gradient method to find time differences as fault feature. Fault condition with the various location, type and resistance are being simulated to get the fault feature as input data for k-NN training. The method is being tested by using random fault location and fault type. The test result indicates that the proposed method has high accuracy in estimating fault location.

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