Fault locating in large distribution systems by empirical mode decomposition and core vector regression

Abstract

This paper proposes an intelligent fault locating method using a new signal analysis technique called Empirical Mode Decomposition (EMD) and Core Vector Regression (CVR) for large distribution systems. The conventional fault locators are based on the measurement of post-fault line impedance suffering from the factors such as path fault impedance, system configuration and line loading, so that they have low accuracy. On the other hand, because of the vast range of resistances, the negative impact of damping factors affects the performance of travelling wave-based fault locators in large distribution systems. To overcome these problems, this paper uses a minimum measuring device to meet the acceptable observation of transient waves and presents a novel method for locating phase to ground faults in a large distribution system using CVR. Inspecting the energy content of transient voltage around the path characteristic frequencies by EMD can provide a suitable fault pattern to CVR. Training of the proposed algorithm needs little time and small amount of memory in comparison with the existing methods. Presented algorithm is examined on IEEE 34-bus test system which shows satisfactory results. Then, the results are compared with the method of recent papers based on Artificial Neural Networks (ANNs).