Robust functional analysis for fault detection in power transmission lines

Abstract

Fault detection methods in power transmission lines aim to detect deviations of the electrical signals from the expected behavior of such signals under normal operating conditions. One approach is to model, as accurately as possible, the expected behavior of the electrical signals under normal operating conditions. Furthermore, even under normal conditions, electrical signals are subject to random noises. Therefore, upper and lower limits must be established. The larger the limits, the harder the fault detection. On the contrary, the narrower the limits the more likely to detect false faults. Functional analysis of power transmission lines was originally proposed to represent the behavior of the electrical signals and to estimate the upper and lower limits under normal operating conditions. Nonetheless, the originally proposed estimates are biased and rely on statistical assumptions that do not hold in practice. This work proposes new methods to estimate the parameters of the functional model and new upper and lower limits that do not rely on specific statistical assumptions. Simulated and real case results show that the proposed robust functional analysis reduces bias and provides more accurate false fault detection rates, as compared to the previous method.