Predictive Contingency Analysis for Real-Time Grid Security
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Solution Overview
Problem
Conventional state estimator algorithms struggle to provide efficient and timely real-time grid security assessments, especially in large power systems with high variability from renewable energy sources, leading to increased risk of grid failures and reduced resilience.
Innovation Solution
An automated, AI-based predictive contingency analysis system that utilizes a trained ML model to generate an augmented contingency list combining user-defined and predicted contingencies with non-zero CPI scores, leveraging historical data for enhanced situational awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional state estimator algorithms are used for real-time grid security assessment, then the system can maintain operational security, but the assessment efficiency and speed are insufficient for large-scale power systems with high renewable energy variability
Solution Approach 1:
The patent replaces conventional state estimator algorithms with a machine learning-based predictive contingency analysis system. The ML model is trained on historical data to predict system states and contingencies, substituting traditional computational methods with intelligent algorithms that process data faster and adapt to renewable energy variability, thereby improving assessment efficiency while maintaining grid security reliability
Solution Approach 2:
The system performs preliminary contingency analysis by predicting potential system states and identifying critical contingencies before they occur. The ML model forecasts future system conditions based on historical patterns, allowing operators to take preventive actions in advance, thus improving both assessment speed and grid security by addressing potential issues before they manifest
2Reliability
If more scenario simulations are conducted to improve grid security and resilience, then the contingency analysis becomes more comprehensive, but the computational time and complexity increase significantly
Solution Approach 1:
The patent extracts and focuses on the most critical contingencies by using the ML model to predict and rank potential system failures. Instead of analyzing all possible scenarios, the system identifies and prioritizes high-risk contingencies with non-zero CPI scores, extracting only the essential cases that require detailed analysis. This approach maintains comprehensive grid security assessment while significantly reducing computational time and complexity
3Reliability
If the contingency list includes all possible equipment outages and user-defined contingencies, then the analysis covers all potential failures, but the size of the contingency list becomes unmanageably large
Solution Approach 1:
The patent applies partial action by generating an augmented contingency list that includes only contingencies with non-zero Contingency Performance Index (CPI) scores predicted by the ML model. Instead of analyzing all possible equipment outages, the system selectively includes partial cases that are most likely to impact grid security. This approach maintains comprehensive coverage of critical failures while keeping the contingency list size manageable for real-time analysis
Data Source
AI summary
A system and method for enhancing real-time grid security of a power system is provided. The method includes receiving real-time data associated with the power system and inputting the real-time data associated with the power system to a trained ML model. The trained ML model is generated based on archived historical data stored in an archival repository. Further, the method includes predicting using the trained ML model a list of contingencies with a non-zero contingency performance index score (Lp) for the real-time data associated with the power system. The method also includes determining an augmented contingency list comprising a user defined contingency list (Lu) and the predicted list of contingencies with the non-zero CPI score (Lp) and performing a real-time predictive contingency analysis on the power system based on the augmented contingency list.