A Novel Fault Classification Approach Using Manifold Learning Algorithm

This paper provides a novel solution for power system transmission line fault classification. It is based on Clarke-Concordia transform and manifold learning algorithm using one-end current signals. We first convert the currents into " 0" phases, and construct the trajectories in a 3-diemensional space. Manifold learning is used to extract characteristic features and identify different fault patterns. A weight factor is introduced in the neighborhood selection algorithm in manifold learning, which helps to solve the local nonlinear confusion problem. Fault pattern formed in 3-dimensional " 0" space better illustrate the fault's distortion and displacement from the normal state. Simulation results have proven the feasibility of this approach. 2011 IEEE.

2011 16th International Conference on Intelligent System Applications to Power Systems

2011 16th International Conference on Intelligent System Applications to Power Systems

A Novel Fault Classification Approach Using Manifold Learning Algorithm Conference Paper