Power Grid Failure Prediction Using Real-Time Security Indexing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting power system failures are manual, time-consuming, and require skilled operators, making it difficult to assess real-time security and prevent cascaded outages and blackouts in a cost-effective and efficient manner.
Innovation Solution
A system and method that uses real-time state estimation data and a trained machine learning model to extract feature vectors and determine a security index, predicting power system failures and their severity by analyzing contingency analysis information from various sources, including SCADA systems and PMUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual contingency analysis methods are used to assess power system security, then the analysis can be performed with basic tools, but the process becomes time-consuming and requires skilled operators for interpretation
Solution Approach 1:
The patent introduces an intermediary processing system that automatically interprets contingency analysis results. This system acts as a mediator between the raw contingency data and the operator, automatically generating security assessments and failure predictions without requiring manual interpretation by skilled operators.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system. Instead of operators manually interpreting contingency results, the system uses automated algorithms and processing to generate security assessments, eliminating the time-consuming manual interpretation step.
2Reliability
If comprehensive contingency simulations are performed for multiple contingencies in real-time, then the security assessment becomes more thorough, but the amount of information to be processed becomes exhaustive
Solution Approach 1:
The patent extracts only the critical and relevant information from comprehensive contingency simulation results. Instead of processing all generated data, the system identifies and extracts key security indicators and failure risk metrics, reducing the information processing burden while maintaining assessment completeness.
Solution Approach 2:
The patent segments the comprehensive contingency analysis into distinct processing stages and information categories. By dividing the exhaustive information into manageable segments (security constraints, failure risks, operational impacts), the system can process and present information more efficiently without losing completeness.
3Productivity
If automated failure prediction systems are implemented in real-time, then the speed and accuracy of failure prediction improves, but the system complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing contingency data and pre-identifying potential failure scenarios before real-time prediction is needed. This preparation work is done in advance, allowing the real-time prediction system to operate more efficiently with pre-organized data structures and pre-computed baseline values.
Solution Approach 2:
The prediction system is designed to be self-sufficient in processing and interpreting its own data. The system automatically generates security assessments and failure predictions without requiring external manual intervention, making the complexity management self-contained and reducing operational burden despite the system's computational sophistication.
Data Source
AI summary
A system and a method for predicting failure in a power system in real-time. The method comprises obtaining, by a processing unit, state estimation data corresponding to electrical quantities of the power system received from one or more sources in real-time, extracting a feature vector from the received state estimation data based on contingency analysis information using a trained machine learning model, wherein the feature vector corresponds to one or more parameters pertaining to the power system in real-time, determining a security index for the received state estimation data based on the extracted feature vector using the trained machine learning model and, predicting a failure of the power system based on the determined security index.