Edge AI for Real-Time Motor Condition Monitoring in Smart Grids
Keywords:
Edge AI, Smart Grid, Motor Condition Monitoring, Federated Learning, Analytics, Edge-Cloud Orchestration, IoT Sensors, Cybersecurity, Real-time Diagnostics, Fault DetectionAbstract
The paper explores the edge Artificial Intelligence (Edge AI) possibilities of real-time monitoring of motor conditions in smart grids and identifies the major challenges on latency, data privacy and the scalability of the systems. The first objective is to build a safe and effective monitoring system based on the foundation of federated learning, intelligent resource coordination, and IoT-powered sensors. The research implements the methodology of secondary research by reviewing 35 peer-reviewed articles in terms of thematic analysis on the topic of architectural models, predictive analytics and security protocols related to cybersecurity. The results indicate that Edge AI can improve Latency in Diagnostic analysis by more than 70% and achieve a classification recognition rate of more than 94%, in addition to localized analysis of anomalies without violating data privacy. Federated learning guarantees adaptive fault analytics, and edge-cloud orchestration makes the energy and computing efficient. Also, the use of smart sensors enables permanent observation of conditions and allowing spotting faults before they occur. The paper establishes that Edge AI based systems offer a scalable and secure course of next-generation smart grid diagnostics and predictive maintenance.
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