This paper presents and evaluates a methodology to detect and diagnose single or multiple faults at their earliest stage in electrical systems. The faults affect the gain, the offset and the phase shifting of the output currents. Following the general Fault Detection and Diagnosis process, the methodology is based on data driven approach for modeling the currents in the time domain, pre-processing with the Park transform and univariate statistical feature extraction and analysis. In the case of incipient faults, the Park transformed currents are more sensitive. Therefore we use their Cumulated Sum (CUSUM) (CUSUM mean or CUSUM variance) for the fault detection. Within the incipient fault ranges ( ) and a threshold set to have zero false alarm rate, intensive simulations show that these features successfully detect the fault(s) with a probability of miss detection around 5%. The classification of the seven fault classes that have been identified (3 single and 4 multiple) is successfully done with Linear Discriminant Analysis and Support Vector Machines (SVM) when data is linearly separable or kernel-based SVM when data is non linearly separable. The simulation results show that the misclassification errors are lower than 3%. For the fault estimation, the slope of the CUSUM decision has been identified as a relevant feature. For the different faults (single or multiple), from the evolution of the slope along with the fault severity, an analytical model has been derived. The inversion of this model allows an accurate estimation of the fault level.