Intelligent Predictive Resilience Management for Power System Equipment

Modern electrical grids require resilient, affordable, and interoperable primary equipment that can handle the growing integration of renewable sources and fluctuating operating conditions. This concept introduces proactive reliability management based on advanced operational data analysis, integrated diagnostic frameworks, and adaptive degradation management strategies. The combination of online measurements, model-based indicators, and intelligent anomaly detection increases equipment operational availability, reduces unplanned outages, and optimizes total life cycle costs. These digital models can work with equipment from different manufacturers and allow for easy integration into existing and future electrical grids.

Our team contribution:

We are a Czech university research team with strong expertise in partial discharge (PD) diagnostics, high-voltage testing and sensor development, offering experimental and validation support for the development of advanced sensors aimed at monitoring the state of insulation in power systems. Our current research includes collaboration on the development of novel sensors for the detection of electrical and thermal phenomena in high voltage components. We operate a high-voltage laboratory (up to 200 kV) equipped with PD calibrators, HF/UHF/acoustic detection systems and aging chambers to enable comparative testing of sensors under laboratory conditions. We specialize in coupling electrical voltage signals (e.g., PD pulses, dielectric response) and provide expertise in system-level testing, sensor calibration, and signal analysis. We are seeking to join consortia focused on sensor platforms for industrial and energy applications, providing critical experimental support and validation for predictive resilience strategies.