Power Grid Risk Scoring Using PINN Vulnerability Prediction
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Solution Overview
Problem
Electrical power grids face challenges in accurately assessing vulnerability and fragility due to natural destructive forces like wildfires, as traditional methodologies lack nuanced differentiation between various causes and fail to leverage advanced analytical tools like explainable AI.
Innovation Solution
A computer-implemented method and system that fuse collected risk data and electrical power grid data to predict vulnerability and fragility metrics using a physics-informed neural network (PINN), integrating these metrics with risk profiles to develop threat metrics and perform corrective actions to mitigate risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methodologies are used to assess vulnerability and fragility, then the assessment process is simple, but the accuracy and differentiation between various causes of natural destructive forces is insufficient
Solution Approach 1:
The patent introduces a physics-informed neural network (PINN) as an intermediary between raw data and vulnerability/fragility assessments. The PINN integrates multiple data sources (grid data, risk data, environmental data) and applies physics-based constraints to generate accurate vulnerability and fragility metrics, resolving the contradiction by providing high measurement precision through a structured intermediate processing layer.
Solution Approach 2:
The assessment system combines multiple data types (grid infrastructure data, risk data, environmental data) and multiple analytical approaches (machine learning, physics-based models) into a composite assessment framework. This composite approach enables differentiated assessment of various natural destructive forces while maintaining systematic organization, thus improving accuracy without overwhelming complexity.
2Reliability
If advanced analytical tools like explainable AI and physics-informed neural networks are implemented, then the accuracy and actionability of risk assessment improves, but the system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the PINN model continuously learns from assessment outcomes and refines its predictions. The system provides feedback loops that adjust vulnerability and fragility metrics based on observed performance, enhancing reliability through iterative improvement while managing complexity through automated adjustment processes.
Solution Approach 2:
The patent replaces traditional mechanical/manual assessment methods with physics-informed neural networks that automatically process data and generate assessments. This substitution transitions from manual, rule-based systems to automated, intelligence-driven systems, improving reliability through consistent application of physics-based principles while the automation manages operational complexity.
3Ease of operation
If comprehensive data fusion and integration are performed to develop threat metrics, then the actionability and targeting of mitigation strategies improves, but the data processing complexity increases
Solution Approach 1:
The patent segments the comprehensive data processing into distinct modules: data collection, data fusion, vulnerability assessment, fragility assessment, and threat metric development. Each module handles specific data types and processing tasks, making the overall complex system manageable through modular organization while still achieving comprehensive integration for actionable insights.
Data Source
AI summary
Systems and methods for a risk mitigation system for electrical power grids. To mitigate risks such as natural destructive forces, collected risk data and EPG data can be fused to obtain fused data. The vulnerability metric and fragility metric of the EPG based on risk profiles generated from the fused data can be predicted with a physics-informed neural network (PINN) trained with the fused data. EPG threat metrics can be developed by integrating the vulnerability metric, fragility metric, and the risk profiles into an integrated score that determines the probability of failure of the EPG caused by natural destructive forces. The present embodiments can perform a corrective action with an automated helper to mitigate the risks to the EPG caused by the natural destructive forces determined from the EPG threat metrics.