An in-field apparatus for automatic localization of a fault having occurred at power transmission lines of a power supply system, said in-field apparatus comprising: (a) a preprocessing unit adapted to process measured voltage and/or current raw time series data of the power transmission lines to provide a normalized raw data and/or feature representation of the measured raw time series data; and (b) an artificial intelligence module, AIM,, characterized by: the artificial intelligence module being configured to predict an evaluation time used for evaluation of the measured voltage and/or current raw time series data to localize the fault based on the normalized raw data and/or feature representation, wherein the artificial intelligence module, AIM, comprises a window detection unit adapted to determine an window size of a window used for feature extraction from the measured voltage and/or current raw time series data, wherein the window detection unit is adapted to take as an input preprocessed fault record values and is adapted to feed this input into an artificial neural network which generates a learned regression curve for every defined window size, and a time point detection unit adapted to determine an evaluation time point for the evaluation of features extracted from the measured voltage and/or current raw time series data, wherein the time point detection unit is adapted to take as an input the predicted normalized impedance values for all measurements points for a single fault record, wherein for every window size the time point detection unit is adapted to apply a moving mean value to smooth the resulting regression curve, and wherein the time point detection unit is adapted to identify the window size and measurement point by minimizing the smoothed normalized impedance values across the window sizes and measurement points. (a) a preprocessing unit adapted to process measured voltage and/or current raw time series data of the power transmission lines to provide a normalized raw data and/or feature representation of the measured raw time series data; and (b) an artificial intelligence module, AIM,, characterized by: the artificial intelligence module being configured to predict an evaluation time used for evaluation of the measured voltage and/or current raw time series data to localize the fault based on the normalized raw data and/or feature representation, wherein the artificial intelligence module, AIM, comprises a window detection unit adapted to determine an window size of a window used for feature extraction from the measured voltage and/or current raw time series data, wherein the window detection unit is adapted to take as an input preprocessed fault record values and is adapted to feed this input into an artificial neural network which generates a learned regression curve for every defined window size, and a time point detection unit adapted to determine an evaluation time point for the evaluation of features extracted from the measured voltage and/or current raw time series data, wherein the time point detection unit is adapted to take as an input the predicted normalized impedance values for all measurements points for a single fault record, wherein for every window size the time point detection unit is adapted to apply a moving mean value to smooth the resulting regression curve, and wherein the time point detection unit is adapted to identify the window size and measurement point by minimizing the smoothed normalized impedance values across the window sizes and measurement points. the artificial intelligence module being configured to predict an evaluation time used for evaluation of the measured voltage and/or current raw time series data to localize the fault based on the normalized raw data and/or feature representation, wherein the artificial intelligence module, AIM, comprises a window detection unit adapted to determine an window size of a window used for feature extraction from the measured voltage and/or current raw time series data, wherein the window detection unit is adapted to take as an input preprocessed fault record values and is adapted to feed this input into an artificial neural network which generates a learned regression curve for every defined window size, and a time point detection unit adapted to determine an evaluation time point for the evaluation of features extracted from the measured voltage and/or current raw time series data, wherein the time point detection unit is adapted to take as an input the predicted normalized impedance values for all measurements points for a single fault record, wherein for every window size the time point detection unit is adapted to apply a moving mean value to smooth the resulting regression curve, and wherein the time point detection unit is adapted to identify the window size and measurement point by minimizing the smoothed normalized impedance values across the window sizes and measurement points.