FLITC (Fault Location Identification and Type Classification) is capable of identifying the short-circuit fault location and classifying the fault type into 11 different categories. The developed models –convolutional neural networks in conjunction with wavelet transformation for optimal preprocessing- grasp the spatial and temporal characteristics of three-phase voltage and current time-series measurements, thereby increasing network visibility in real time. Our team conducted a series of simulations using synthetic data, which we provided in an open-source repository. This is ideal for the research community because the lack of data was the biggest issue during the development phase, which replicated a wide range of fault occurrence scenarios with 11 different types. The results demonstrate the efficacy of the proposed method, reaching an accuracy of 91.4% for fault detection, 93.77% for correct branch identification, 94.93% for fault type classification, and an RMSE value of 2.45% for location calculation. After the model’s finetuning, real data was digested from FEVER pilots to validate our concept in a real environment.