Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12394/18190
Title: Implementation of an Intelligent Ground Fault Protection System for Pump Chambers Using Artificial Intelligence Networks
Other Titles: Implementación de un sistema inteligente de protección contra fallas a tierra para cámaras de bombeo utilizando redes de inteligencia artificial
Authors: Huacho Ichpas, Walter
Rojas Fierro, Danny Javier
Huaman Rojas, Jezzy James
metadata.dc.contributor.advisor: Huamán Rojas, Jezzy James
Keywords: Instalaciones eléctricas
Electrical installations
Minería subterránea
Underground mining
Circuitos
Circuits
Lora
Lora
Publisher: Universidad Continental.
Issue Date: 2025
metadata.dc.date.available: 7-Oct-2025
Citation: Huacho, W., Rojas, D. J., & Huamán, J. J. (2025). Implementation of an Intelligent Ground Fault Protection System for Pump Chambers Using Artificial Intelligence Networks [Tesis de licenciatura, Universidad Continental]. Repositorio Institucional Continental. https://repositorio.continental.edu.pe/handle/20.500.12394/18190
metadata.dc.identifier.doi: https://doi.org/10.14445/23488379/IJEEE-V12I6P116
Abstract: Abstract - Extreme environmental conditions in underground mining environments, such as high relative humidity and thermal fluctuations, can lead to erroneous activations of ground fault protection relays, thereby compromising the operational continuity of critical systems even in the absence of actual electrical faults. This study introduces an embedded solution based on Artificial Intelligence of Things (AIoT), designed to detect false positives in underground pumping chambers located at altitudes exceeding 4000 meters above sea level. The proposed system integrates environmental sensors with a microcontroller that executes a Gated Recurrent Unit (GRU) neural network model in real-time, trained on 14400 samples collected over a continuous 10-day period. In contrast to prior approaches, the developed architecture performs local inference without relying on constant connectivity and transmits alerts using LoRa technology. System evaluation yielded an overall accuracy of 96.0%, with a precision and sensitivity of 78.6% for the false positive class, and an AUC of 0.99. These findings effectively reduce false activations and improve operational continuity. The proposed solution offers a cost-effective and replicable approach to optimizing electrical safety in industrial areas with restricted connectivity.
metadata.dc.relation: https://www.internationaljournalssrg.org/IJEEE/paper-details?Id=1087
Extension: p. 187-194
metadata.dc.rights.accessRights: Acceso abierto
metadata.dc.source: Universidad Continental
Repositorio Institucional - Continental
Appears in Collections:Tesis

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