AI Digital Twin for Power System Anomaly Detection
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Solution Overview
Problem
Current power system analysis methods are inefficient and labor-intensive, requiring manual processes for conducting scenarios with load and generation variations, predicting system anomalies, and performing security assessments, which hampers system reliability and security.
Innovation Solution
The implementation of AI/ML-based systems that utilize decentralized technologies like ADMS, GIS, and OSI-PI to automate the development of software electrical network models, derive data models from historical data, and conduct analyses for predicting system behavior and forecasting applications, using deep-encoder models and machine learning for anomaly detection and predictive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for conducting power system scenarios and security assessments, then system analysis can be performed with existing tools, but the process is labor-intensive and inefficient
Solution Approach 1:
The system enables automated self-service through AI/ML models that independently perform power system scenario analysis, anomaly detection, and security assessments without requiring manual intervention. The digital twin automatically processes historical data, conducts simulations, and generates predictions, freeing operators from labor-intensive manual analysis while improving productivity and reducing time loss.
2Loss of information
If decentralized technologies and platforms are integrated, then data availability improves, but system complexity increases
Solution Approach 1:
The patent merges multiple decentralized technologies and platforms (ADMS, GIS, MDM, OSI-PI) into a unified digital twin framework. By integrating these disparate data sources and systems into a single cohesive AI/ML-based platform, the invention achieves complete data availability while managing complexity through centralized model architecture that automatically harmonizes inputs from various sources.
Solution Approach 2:
The digital twin system performs multiple functions including scenario analysis, anomaly detection, predictive maintenance, and security assessments within a single unified platform. This multi-functional approach consolidates what would otherwise require separate specialized systems, reducing overall complexity while maintaining comprehensive data integration from decentralized sources.
3Measurement precision
If AI/ML models are trained with historical data, then prediction accuracy improves, but computational requirements and model development time increase
Solution Approach 1:
The system performs preliminary action by pre-training AI/ML models with historical power system data during off-peak periods or initial setup phases. This advance preparation creates ready-to-use digital twins that can immediately provide accurate predictions when deployed, separating the time-consuming training phase from the operational phase and thus improving prediction accuracy without impacting operational response time.
Data Source
AI summary
Some embodiments relate to systems and methods for analyzing an electrical network. For example, a system for analyzing an electrical network may include a memory and a processor, coupled to the memory. The processor is configured to execute instructions from the memory causing the processor to: perform power system studies to improve system reliability and security using historical data of past power system operations, derive a data model of the power system using artificial intelligence, and using the data model and training with the power system analysis data to conduct analyses for predicting at least one of system behavior and forecasting applications, and report the at least one of system behavior and forecasting applications.