Dynamic Virtual Model for Electrical System Predictive Analytics
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Solution Overview
Problem
Current systems lack an automatic and intelligent solution for real-time monitoring and visualization of electrical power system health, performance, and reliability, relying on static models that cannot adjust to operational changes or age with the facility, leading to inaccurate predictions and increased operational costs.
Innovation Solution
A system comprising a data acquisition component, power analytics server, and client terminal that automatically generates a schematic user interface by acquiring real-time data, using a virtual system modeling engine, analytics engine, and machine learning engine to synchronize and calibrate a virtual system model with actual operational data, providing real-time predictive analytics and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static system models are used for monitoring and prediction, then system complexity is reduced and ease of operation is improved, but the accuracy and reliability of predictions deteriorate because the models cannot adjust to operational changes or age with the facility
Solution Approach 1:
The patent implements a dynamic virtual system model that automatically updates itself in real-time based on actual operational data from sensors. The model transitions from a static state to a dynamic state where it continuously adapts to changing operational conditions, equipment aging, and system modifications without requiring manual intervention, thereby maintaining both ease of operation and prediction accuracy
Solution Approach 2:
The virtual system model performs self-calibration and self-updating by automatically comparing predicted values with actual sensor measurements and adjusting its parameters accordingly. This self-service capability eliminates the need for manual model updates while maintaining high prediction accuracy as the system evolves over time
2Measurement precision
If manual system modeling and updating is performed, then manufacturing precision and measurement precision are improved, but productivity decreases and loss of time increases due to the labor-intensive nature of keeping models synchronized with operational changes
Solution Approach 1:
The patent replaces manual mechanical processes of model updating with an automated computational system. The virtual system model uses algorithmic processes to automatically ingest sensor data, compare predictions with measurements, and update model parameters, substituting human labor with automated computing operations that maintain precision while dramatically improving productivity
Solution Approach 2:
The system implements continuous feedback loops where sensor measurements are compared with model predictions, and the differences are used to automatically adjust model parameters. This closed-loop feedback mechanism maintains high measurement precision while eliminating manual intervention, thereby improving productivity and reducing time loss
3Reliability
If comprehensive real-time monitoring and predictive analytics are implemented, then reliability and measurement precision are improved, but device complexity increases due to the multiple engines and components required
Solution Approach 1:
The patent combines multiple functional components (virtual system modeling engine, sensor data acquisition, calibration engine, and schematic generation) into an integrated automated system. The virtual system model serves as a central hub that coordinates data flow and processing across all components, reducing overall system complexity while maintaining comprehensive monitoring and predictive analytics capabilities
Solution Approach 2:
The virtual system model performs multiple functions simultaneously: it predicts system behavior, compares predictions with actual measurements, calibrates itself, generates updated models, and creates schematic representations. This multi-functionality reduces the need for separate dedicated systems for each task, thereby improving reliability without proportionally increasing device complexity
4Loss of time
If automatic real-time model synchronization is implemented, then loss of time is reduced and productivity is improved, but device complexity and use of energy increase due to continuous data acquisition and processing
Solution Approach 1:
The system implements periodic sampling of sensor data and model updates at optimized intervals rather than continuous monitoring. The virtual system model automatically adjusts the sampling frequency based on system dynamics and prediction confidence, reducing energy consumption while maintaining timely predictions and minimizing time loss
Data Source
AI summary
A system for automatically generating a schematic user interface of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system. The schematic user interface creator engine is configured to create a schematic user interface that is representative of the virtual system model and link the schematic user interface to the data acquisition component.