Safety Fault Prediction and Diagnosis of Power Measurement Equipment Based on 6G Big Data Analysis

Abstract

The advent of 6G networks has revolutionized power system monitoring by enabling ultra-fast, low-latency communication, which is essential for real-time fault prediction and diagnosis in power measurement equipment. However, conventional fault diagnostic methods often rely on centralized data processing, which raises significant concerns about data privacy threats, latency, and inefficiencies in real-time problem identification. We provide a Big Data-Driven Predictive Analytics with Federated Learning (BD-PA-FL) platform to address these issues. Without sending sensitive raw data, this novel method enables decentralized, privacy-preserving model training across numerous edge devices. By utilizing distributed big data and safeguarding data privacy, BD-PA-FL enables decentralized predictive analytics through FL. It avoids centralized data pooling, which lowers latency and improves real-time, privacy-aware fault detection in contrast to traditional fault diagnosis. To enable effective and intelligent fault prediction at the network edge, the proposed framework incorporates several essential elements. First, vital operating metrics from power equipment are captured by real-time sensor data collection. After that, insightful feature extraction methods are employed to identify significant patterns in the unprocessed data, enabling the detection of anomalies at an early stage. FL algorithms allow the system to collaboratively train predictive models across distributed nodes without sharing sensitive data, preserving privacy. By leveraging a cloud-edge AI architecture, the system ensures scalability, low latency, and effective resource utilization for predictive maintenance. Experimental results confirm that the BD-PA-FL framework significantly improves fault detection accuracy, reduces downtime, and enhances overall grid reliability in a secure, 6G-enabled environment.