Research on Sensor Network Optimization for Distribution Equipment Condition Monitoring

To address the issues of unscientific deployment, high energy consumption, and lack of monitoring for special conditions in distribution equipment sensor networks, this study proposes a comprehensive optimization scheme integrating conditional value at risk (CVaR) robustness model, improved I-Greedy algorithm, and IoT architecture. The CVaR model enhances deployment robustness, and the optimized network is applied to three-level insulation monitoring for live cleaning and high-impedance fault identification, unifying network performance and scenario adaptation. Simulation and field verification show that the proposed scheme significantly improves the comprehensive performance and reliability of monitoring networks, providing an effective technical approach to solve the perception bottleneck in intelligent distribution network operation and maintenance.