Dynamic Power System Model Calibration via Real-Time Sensor Feedback
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Solution Overview
Problem
Current electrical power system modeling techniques lack real-time synchronization with actual operational conditions, leading to inaccurate reliability and performance predictions due to static models that cannot adapt to daily changes or age with the system, resulting in potential failures and increased operational costs.
Innovation Solution
A system comprising a processor, memory, display, and input device configured to model an electrical power system topology using a component database, control engine, and topology modeling engine, allowing for real-time data integration and synchronization with actual system conditions, enabling intuitive modeling and predictive analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static modeling techniques are used, then model simplicity is maintained, but real-time synchronization with actual operational conditions deteriorates
Solution Approach 1:
The patent transforms static power system models into dynamic models that automatically update with real-time operational data. The system continuously synchronizes model parameters (load demands, generation outputs, component statuses) with actual field measurements, enabling the model to adapt and age alongside the physical system while maintaining predictive accuracy for reliability assessments.
2Measurement precision
If real-time data integration is implemented, then predictive accuracy is improved, but system complexity increases
Solution Approach 1:
The system implements self-updating capabilities where the power system model automatically synchronizes itself with real-time operational data without requiring manual intervention. The model autonomously integrates field measurements, updates component aging states, and recalibrates predictions, reducing the need for complex manual data management while maintaining high predictive accuracy.
Solution Approach 2:
The patent establishes feedback loops where real-time operational data flows into the model, which then generates updated reliability predictions and operational recommendations. This continuous feedback mechanism enables the system to learn from actual performance data and improve predictions over time, balancing accuracy requirements with manageable system complexity.
3Ease of operation
If static models are used, then ease of operation is maintained, but adaptability to daily changes deteriorates
Solution Approach 1:
The system maintains ease of operation by providing a user-friendly interface that presents simplified reliability metrics and recommendations, while internally implementing dynamic adaptation to daily operational changes through automatic data synchronization and model updating, thus combining simplicity with adaptability.
4Reliability
If models age with the system, then reliability predictions improve, but data processing requirements increase
Solution Approach 1:
The system performs preliminary data processing and model updates in advance, continuously synchronizing the power system model with operational data during normal operations. This preparation ensures that when reliability predictions are needed, the model is already up-to-date, reducing actual processing time while maintaining accurate aging-based predictions.
Data Source
AI summary
Systems and methods for modeling an electrical power system are disclosed. A computer and an analytics server are in network connection. The computer comprises a processor coupled with a memory. The memory is configured to maintain at least one engine element and a component database. The analytics server comprises a virtual system modeling engine and an analytics engine. The component database is operable to store power system components. The at least one engine element is operable to generate a virtual system model of the electrical power system and generate predicted output based on the virtual system model. The analytics engine is operable to monitor the predicted output and real-time output from at least one sensor of the electrical power system, and calibrate the virtual system model based on a difference between the predicted output and the real-time output.