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Prediction of Faults Location and Type in Electrical Cables Using Artificial Neural Network

Author

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  • Ana-Maria Moldovan

    (Faculty of Building Services Engineering, Technical University of Cluj-Napoca, 28 Memorandumului Str., 400114 Cluj-Napoca, Romania)

  • Mircea Ion Buzdugan

    (Faculty of Building Services Engineering, Technical University of Cluj-Napoca, 28 Memorandumului Str., 400114 Cluj-Napoca, Romania)

Abstract

Detecting and locating faults in electrical cables has been a permanent concern regarding electrical power distribution systems. Over time, several techniques have been developed aiming to manage these faulty situations in an efficient way. These techniques must be fast, accurate, but, above all, efficient. This paper develops a new approach for detecting, locating, classifying, and predicting faults, particularly in different types of short-circuits in electrical cables, based on a robust artificial neural network technique. The novelty of this approach lies in the ability of the method to predict fault’s location and type. The proposed method uses the Matlab and Simulink platform and comprises four consecutive stages. The first one is devoted to the development of the Simulink model. The second one implies a large number of simulations in order to generate the necessary dataset for training and testing the artificial neural network model (ANN). The following stage uses the ANN to classify the location and the type of potential faults. Finally, the fourth stage consists of predicting the location and the type of future faults. In order to reduce the time and the resources of the simulation process, a virtual machine is used. The study reveals the efficiency of the method, and its ability to successfully predict faults in real-world electrical power systems.

Suggested Citation

  • Ana-Maria Moldovan & Mircea Ion Buzdugan, 2023. "Prediction of Faults Location and Type in Electrical Cables Using Artificial Neural Network," Sustainability, MDPI, vol. 15(7), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6162-:d:1115030
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