An application of global sensitivity analysis in evaluation of transmission line fault-locating algorithms

Computation of distance to fault on an electrical transmission line is affected by many sources of uncertainty, including parameter setting errors, measurement errors, as well as absence of information and incomplete modelling of a system under fault condition. In this paper we propose an application of the variance-based global sensitivity measures for evaluation of fault location algorithms. The main goal of the evaluation is to identify factors and their interactions that contribute to the fault locator output variability. The sensitivity measures are calculated using the sparse grid integration approach that requires smaller number of samples compared to quasi-Monte Carlo integration. In practice, such analysis can help in: selection of the optimal fault location algorithm (device) for a specific application, calibration process and building confidence in a fault location result. The paper concludes with application examples which demonstrate use of the proposed methodology in testing and comparing some commonly used fault location algorithms.