Machine Learning-Based Real-Time Fault Prediction: Enhancing Distribution Transformer Health Monitoring System
Keywords:
Transformer, Condition Monitoring Technique, Internet of Things (IoT), Machine learning, Fault detectionAbstract
Addressing the critical concern of real-time monitoring for transformers to mitigate potential operational problems due to damages, this paper highlights the substantial costs linked with maintenance and replacement, posing significant challenges. To address this, an IoT-based monitoring system is devised, ensuring continuous health assessment by tracking Voltage, Current, Temperature, and load capacity. The collected data is sent for analysis to a central server, offering insights into the broader electrical system's performance. IoT integration strengthens security, provides accurate environmental insights, and facilitates early fault detection, enabling prompt repairs and minimizing system failures. In contrast, traditional manual monitoring struggles to detect subtle changes, while IoT-driven remote monitoring requires a robust centralized data infrastructure and real-time transmission, preventing major faults and ensuring equipment protection. This approach reduces risks through centralized remote transformer data collection, complemented by machine learning techniques for proactive flaw prediction.
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