Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/4021 2019 2019 Electrical and Mechanical Engineering School, University of Veracruz, Mexico Aldana Aldana-Franco Electrical and Mechanical Engineering School, University of Veracruz, Mexico Aldana-Franco. R. Electrical and Mechanical Engineering School, University of Veracruz, Mexico Leyva-Retureta, J.. G. Electrical and Mechanical Engineering School, University of Veracruz, Mexico Álvarez-.Sánchez E.. J. Electrical and Mechanical Engineering School, University of Veracruz, Mexico López-Velázquez. A. Electrical and Mechanical Engineering School, University of Veracruz, Mexico Aldana-Franco. F. The transmission of energy is one of the main tasks of Electrical Engineering. Transmission lines are used for this purpose, which are susceptible to various problems such as short-circuit, overload, open circuit, and complex faults. From the perspective of smart grids, one of the open challenges is to have autonomous systems that allow the detection, classification, and location of faults in transmission lines. On the other hand, Artificial Neural Networks are computational tools used in classification and control tasks to be applied to different plants and systems. There are several ways to solve problems using ANNs; one is modularity. This strategy consists of dividing the problem into components that are easier to classify. In this way, a modular system is proposed that is composed of three ANNs: One for detection, one for classification, and one more for the location of faults in transmission lines. A simulation model of a three-phase electrical power system was built using Simulink MATLAB, employing a data transmission approach typical of smart grids. Supervised learning and WEKA software were used for network training. Databases were created using the potential difference and line current, as well as the ground fault impedance. The database was developed through cases and mathematical models, and the performance of the networks was evaluated in the simulated model. The results show that the proposed model allows the identification of all cases presented in the test stage (100%), which is a better performance than a single neural network (81.25%) that is responsible for detecting, classifying, and locating faults. 2026 2026 122 129 10.54216/JISIoT.180209 https://www.americaspg.com/articleinfo/18/show/4021