In , a Bayesian selectivity technique has been introduced to identify the faulty feeder in compensated medium voltage (MV) networks. The proposed technique has been based on a conditional probabilistic method applied on transient features extracted from the residual currents only using the Discrete Wavelet Transform (DWT). In this paper, the performance of this selectivity technique is evaluated when the current transformers (CTs) impacts are considered. The CTs are modeled considering their frequency characteristics. Furthermore, network noises are added to the simulated signals. Therefore, the algorithm can be tested at different practical conditions, such as nonlinear characteristics of the measuring devices and the impact of noise as well. The fault cases occurring at different locations in a compensated 20 kV network are simulated by ATP/EMTP. Results show a reduction in the algorithm sensitivity with considering CT and noise effectiveness.