Alarm Filtering via Virtual Model Comparison in Power Analytics
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Solution Overview
Problem
Current systems lack the ability to provide real-time, accurate monitoring and management of electrical power systems, leading to inadequate predictive failure analysis and operational decision-making due to the inability to synchronize with actual system conditions and age with the facility, resulting in unreliable reliability indices and increased operational costs.
Innovation Solution
A system comprising a data acquisition component, a power analytics server with a virtual system modeling engine, analytics engine, and decision engine, which filters and interprets real-time sensory data from electrical systems by comparing it to predicted data, updating the virtual system model, and communicating alarm conditions for improved understanding and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time sensory data from multiple data points is continuously monitored and displayed, then comprehensive system monitoring is achieved, but information overload occurs making it impossible for operators to understand system status
Solution Approach 1:
The patent extracts only the most critical and relevant information from the vast amount of sensory data. The system identifies and displays only significant alarm conditions and system status changes, filtering out redundant information. This extraction principle allows operators to focus on critical issues without being overwhelmed by comprehensive but incomprehensible data streams.
Solution Approach 2:
The patent applies local quality by providing different levels of information detail to different user roles and contexts. Critical alarm conditions receive prominent display with immediate attention, while routine operational data is presented in summarized formats. This differentiated information presentation ensures that each type of data receives the appropriate level of emphasis based on its operational significance.
2Device complexity
If static system models are used for predictive analysis, then system design can be simplified, but the models cannot adjust to daily operational changes or age with the facility
Solution Approach 1:
The patent transforms static system models into dynamic models that continuously adapt to changing operational conditions. The system automatically updates model parameters based on real-time sensory data, allowing the virtual model to reflect current system state, daily operational changes, and aging effects. This dynamic adaptation maintains model accuracy without requiring excessive complexity.
Solution Approach 2:
The patent implements feedback mechanisms where real-time operational data flows back into the system model to continuously refine and update it. The comparison between predicted model behavior and actual sensory data creates a feedback loop that automatically adjusts model parameters, enabling the system to learn and adapt to changing conditions while maintaining manageable complexity through automated adjustments.
3Loss of information
If traditional alarm systems display all alarm conditions, then complete system status is provided, but operators cannot distinguish critical from non-critical alarms
Solution Approach 1:
The patent applies local quality by differentiating alarm presentation based on criticality level. Critical alarms receive prominent display with immediate visual attention, detailed information, and urgent notification methods. Non-critical alarms are presented in summarized formats with less visual emphasis. This differentiated presentation maintains complete alarm information while enabling operators to quickly identify and prioritize critical issues.
Solution Approach 2:
The patent segments alarm information into distinct categories and priority levels. By dividing the alarm stream into critical, warning, and informational categories, the system allows operators to focus on high-priority issues while maintaining awareness of complete system status. This segmentation enables both comprehensive monitoring and precise criticality differentiation simultaneously.
Data Source
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 sewer 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-lime 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.