Virtual System Model for Electrical Alarm Filtering
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Solution Overview
Problem
Current systems lack real-time operational monitoring and management capabilities, leading to inaccurate predictions of electrical system reliability and performance due to static models that cannot adjust to changing conditions, resulting in potential failures and increased operational costs.
Innovation Solution
A system that includes a data acquisition component, a power analytics server with a virtual system modeling engine, an analytics engine, and a decision engine to filter and interpret real-time sensory data from electrical systems, updating virtual models based on actual conditions and comparing them to real-time data to identify alarm conditions and improve predictive analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static system models are used for predictive analysis, then system design and implementation can be improved through offline simulation, but the models cannot adjust to changing operational conditions leading to inaccurate predictions
Solution Approach 1:
The patent transforms static system models into dynamic virtual models that continuously update themselves in real-time based on incoming operational data. The virtual model evolves from a fixed design-time representation to a living digital twin that adapts its parameters and behavior to match actual system conditions, thereby resolving the contradiction between model stability and adaptability.
Solution Approach 2:
The system implements continuous feedback loops where real-time sensor data from the physical system is compared against the virtual model predictions. Discrepancies trigger model updates and recalibrations, ensuring the virtual model remains synchronized with actual system behavior. This feedback mechanism enables the model to adapt to changing conditions while maintaining prediction accuracy.
2Loss of information
If real-time data acquisition and monitoring are implemented, then operational status can be tracked continuously, but the volume of data overwhelms operators making it difficult to identify relevant information
Solution Approach 1:
The virtual model serves as an intermediary between the complex real-time data stream and the human operator. It processes, synthesizes, and interprets raw sensor data, presenting information in a simplified yet comprehensive format that maintains completeness while improving operability. The virtual model acts as a cognitive bridge that translates overwhelming data into actionable insights.
Solution Approach 2:
The system segments the overwhelming stream of real-time data into meaningful categories and parameters that are processed individually through the virtual model. By dividing the data processing task into manageable segments corresponding to different system components and parameters, the system maintains complete information while making it accessible and understandable to operators.
3Measurement precision
If comprehensive monitoring of all system parameters is performed, then complete system health assessment is achieved, but the complexity of the monitoring system increases
Solution Approach 1:
Instead of implementing complex physical monitoring infrastructure for every system parameter, the patent creates a virtual copy (digital twin) of the entire system. This virtual model replicates system behavior and parameters computationally, allowing comprehensive monitoring through simulation and data comparison rather than through elaborate physical sensing networks, thereby reducing overall system complexity.
Solution Approach 2:
The virtual model serves multiple functions simultaneously: it predicts system behavior, monitors health parameters, identifies anomalies, and provides diagnostic information. This multi-functional approach eliminates the need for separate specialized systems for each monitoring task, reducing overall system complexity while maintaining comprehensive monitoring capabilities.
Data Source
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AI summary
A system for filtering and interpreting real-time sensory data from 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, and a decision 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 decision engine compares the real-time data output against the predicted data output to filter out and interpret indicia of electrical system health and performance. The client terminal is communicatively connected to the power analytics server and configured to display the filtered and interpreted indicia.