End User
ELES
Description
This service will leverage AI technologies for evaluation of the grid stability, based on the grid state graphs and evaluation of likelihood of the short circuit events in the neighbourhood of specific node.
Core Capabilities
Monitoring & Anomaly Detection
Predictive & Prescriptive Analytics
Business Need
This service addresses a critical scientific and operational challenge in the energy sector: real-time evaluation of grid stability. It provides grid operators and energy management companies with a predictive assessment of potential failures by analyzing the likelihood of accidents across the network.
Using historical incident data and real-time measurements from grid connections, the system evaluates the stability of each node within the energy grid. It then calculates an overall stability score, enabling proactive decision-making to prevent outages or disruptions.
The solution is particularly valuable for:
Energy utilities and transmission system operators (TSOs) monitoring large-scale electrical networks.
Smart grid technology providers integrating predictive maintenance and safety features.
Energy research institutions studying grid reliability and resilience.
Government agencies or regulators overseeing energy infrastructure and safety standards.
The model is based on input data from IEEE 39-bus system graphs and real-time Energy Grid State Graphs, ensuring applicability to both simulated environments and operational grids.
Using historical incident data and real-time measurements from grid connections, the system evaluates the stability of each node within the energy grid. It then calculates an overall stability score, enabling proactive decision-making to prevent outages or disruptions.
The solution is particularly valuable for:
Energy utilities and transmission system operators (TSOs) monitoring large-scale electrical networks.
Smart grid technology providers integrating predictive maintenance and safety features.
Energy research institutions studying grid reliability and resilience.
Government agencies or regulators overseeing energy infrastructure and safety standards.
The model is based on input data from IEEE 39-bus system graphs and real-time Energy Grid State Graphs, ensuring applicability to both simulated environments and operational grids.
Key Performance Indicators
Classification accuracy
Incident likelihood confidence
Reduction in required time for analysis (%)
Data Provided
Quasi-real-time and historical data from protection relays
Historical operation logs
TEF
TEF TSO