Abstract
Medium-voltage distribution network faults are closely related to users' power consumption, so timely and effective early warning of distribution network faults is of great significance to ensure the safe and stable operation of the power grid. Aiming at the characteristics of distribution network, this paper presents a fault early warning method for medium voltage distribution network using teaching and learning to optimize non-linear state estimation (TLBO-NSET). Data related to faults are collected, and the data of the same time are regarded as a fault scenario state of the line. The historical data are sorted together to form a fault state set. For the future fault state, TLBO is used to optimize the middle weight of the nonlinear state estimation to predict the future fault level. The effectiveness of this method in distribution network early warning is validated by the analysis of 10 KV medium voltage line fault in a certain area in recent two years.
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