A Practical Fault Location Algorithm With Increased Adaptability for Active Low Voltage Grids

Quick power restoration has always been one of the grid operators' major goals and could be facilitated by the application of automatized fault location methods. The need for such methods is particularly visible in the low voltage (LV) grids which have been overlooked until now. Hence, this article presents a novel turnkey solution to the fault location problem in LV grids. The proposed algorithm takes advantage of the growing data availability and employs a machine learning (ML) model for the prediction of the faulted point. In order to ensure the applicability of the method all the steps of its implementation are analyzed. The study begins by addressing the algorithm's data collection requirements and proposing a smart storage strategy. It continues with the implementation of advanced data processing and minimization techniques, such as the feature selection, and addresses the generalizability limitations of ML–based algorithms by proposing a retraining scheme. Finally, the algorithm's performance during load changes and data contamination is studied. For the validation of the algorithm a modified version of the CIGRE European LV benchmark was simulated. The results verify the algorithm's robustness against the main influencing parameters and reveal low mean absolute errors throughout the tests; the default study case error was 0.5 m.