Analysis of the monitoring and identification effect of system cognitive service technology on DC system in power grid

In contemporary power grid infrastructure, the stability and health of DC systems are critical for uninterrupted energy delivery. As these systems become more complex, traditional monitoring methods are inadequate for detecting early warning signs and critical failures. Integration of cognitive service technologies provides promising capabilities for intelligent monitoring and fault detection in such systems. Despite the availability of raw sensor data, power grid operators struggle to accurately identify and predict faults in DC systems in real-time. The absence of intelligent classification and predictive mechanisms frequently results in a delayed response to system abnormalities, jeopardizing operational reliability. This research aims to develop a machine learning-based monitoring and identification framework for evaluating the operational status of DC systems using sensor-driven datasets. The primary goal is to predict the system’s health status—Healthy, Fault Detected, or Critical Fault—using electrical and environmental parameters. A new algorithm, SmartDC-FaultMonitor, is proposed for analyzing the SmartDC-Monitoring Dataset, which includes voltage, current, temperature, battery condition, communication signal strength, fault alarms, and load status. The methodology includes data preprocessing (missing value handling, encoding, and normalization), hybrid feature selection using Mutual Information and Recursive Feature Elimination (RFE), and classification with an ensemble voting classifier that combines a Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and TabNet. Model tuning is done using grid search, and performance is measured on a hold-out test set. The proposed ensemble model achieved high-performance metrics on the test dataset, with an accuracy of 94.00%, precision of 93.75%, recall of 94.50%, F1-score of 94.12%, and a Matthews Correlation Coefficient (MCC) of 0.91. These results demonstrate the model’s ability to accurately classify system health statuses, including the early detection of critical faults. The study confirms the effectiveness of cognitive service technology in improving the monitoring and identification of DC power grid systems. The SmartDC-FaultMonitor algorithm provides a dependable and scalable approach for real-time fault detection, giving grid operators timely insights and enabling proactive maintenance in smart energy infrastructures.