Research and Application of Innovative Smart Sensors and AI-based Diagnostic, Prognostic and Prescription Software for Series Capacitors

To overcome these limitations, an R&D project was launched to develop an innovative solution capable of detecting incipient capacitor faults and supporting predictive and optimal maintenance.

The project included: (i) experimental characterization of capacitor units under healthy and faulty conditions, measuring parameters such as capacitance, dissipation factor, frequency response, and dielectric strength; (ii) numerical simulations of SCB behavior under different load levels and failure modes, defining hardware accuracy needs and generating synthetic data for AI models; (iii) field tests of wireless communication technologies to identify the most robust option for energized SCB operation; and (iv) development and validation of dedicated electronic hardware, embedded software,

AI algorithms, and an online monitoring platform. The resulting solution integrates a Smart Sensor— installed at line potential, powered by energy harvesting, with wireless communication and embedded

AI—and an SCB Management Software platform that consolidates sensor data and produces global

SCB diagnostics, prognostics and maintenance recommendations. Together, they enable early detection of anomalies, forecasting failure progression, and recommending optimized maintenance actions. This approach delivers significant benefits: reduced SCB downtime, fewer maintenance shutdowns, improved asset management, enhanced system reliability, and lower operational risks and costs for transmission utilities. The article summarizes the methodologies, results, and practical lessons learned throughout the development and field validation of this advanced SCB monitoring solution.