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In Nordic countries, distribution networks are unearthed or compensated. Earth faults, particularly in compensated networks, provide fault currents that are low compared to the load currents. Identification of the faulty feeder is therefore difficult. A preliminary description of discrete wavelet transform (DWT)-Bayesian selectivity technique was introduced in reference 1 to identify the faulty feeder. It was dependent on a conditional probabilistic method applied to transient features extracted by using the DWT. However, a practical setting for this technique has not yet been presented. Furthermore, its sensitivity is limited to 1.5-k fault resistances, and is further reduced to 170 when considering current transformer and network noise. In this paper, the ratio between the absolute sums of the DWT detail level from each feeder is used as an input to the conditional probability approach, providing an enhanced selectivity decision. This input contributes to discriminating the faulty feeder during high resistance faults. The relay setting is introduced as a function of the number of feeders and their characteristic impedances, as the proposed algorithm is dependent on the discharge's initial transients. The performance is evaluated taking into account current-transformer and network noises. A digital implementation is experimentally verified by using two digital signal processing boards.