Implementation of an Intelligent Ground Fault Protection System for Pump Chambers Using Artificial Intelligence Networks

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.