Adjustment strategy of a learning‐based faulty zone locator for distribution systems

Abstract

Fault location is a functionality of the advanced distribution automation for smart grids. This functionality is considered in this paper using a learning-based faulty zone locator (LBFZL), which requires the adequate selection of its adjustment-variables. As these variables are strongly interrelated, the strategy proposed in this paper uses an iterative approach to determine their fair values based on sampling and learning theories. The proposed strategy guides the adjustment process considering the LBFZL performance. According to the analysed tests, the results present a successful selection of the five defined adjustment-variables, due to the high performance in locating single-phase, phase-to-phase, and three-phase faulty zones, in a 34.5 kV-75 bus distribution system. As a result of the proposal application, satisfactory values for the five adjustmentvariables are obtained; and finally, the expected LBFZL performance is higher than 95% for all fault types.