Real-Time Power Capacity Assessment Using Virtual System Modeling
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Solution Overview
Problem
Current computer simulation techniques for electrical power systems lack real-time monitoring and predictive capabilities, leading to inaccurate failure predictions and increased operational costs due to the inability to account for actual system aging and configuration changes.
Innovation Solution
A system comprising a data acquisition component, power analytics server, and client terminal that utilizes real-time data to update a virtual system model, incorporating machine learning to forecast power capacity and predict system performance under contingency events, ensuring synchronization with actual system conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional rigid simulation models are used for electrical power systems, then system design and analysis can be performed, but the models cannot accurately reflect actual system aging and configuration changes in real-time
Solution Approach 1:
The patent transforms the static simulation model into a dynamic virtual system model that continuously updates in real-time. The virtual model synchronizes with the actual power system by receiving real-time operational data, configuration changes, and aging information, allowing it to adapt its parameters and predictions dynamically rather than relying on fixed design-stage data
Solution Approach 2:
The system implements a feedback mechanism where real-time data from sensors and monitoring devices in the actual power system is continuously fed back to update the virtual system model. This feedback loop ensures the virtual model remains synchronized with the actual system state, including aging effects and configuration changes, thereby improving prediction accuracy
2Reliability
If real-time data acquisition and virtual model synchronization are implemented, then accurate predictive analysis can be achieved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent creates a virtual copy (virtual system model) of the actual power system that mirrors its structure, parameters, and behavior. This digital twin approach allows comprehensive monitoring and prediction without physically modifying the actual system, reducing operational complexity while maintaining high reliability through accurate virtual representation
Solution Approach 2:
The virtual system model serves multiple functions simultaneously: it acts as a real-time monitoring platform, a predictive analysis tool, a training environment, and a what-if simulation platform. This multi-functionality consolidates multiple systems into one unified platform, reducing overall system complexity while enhancing reliability
3Productivity
If comprehensive real-time monitoring and predictive analysis are implemented, then operational costs can be reduced through timely maintenance suggestions, but initial system investment and infrastructure costs increase
Solution Approach 1:
The system enables self-service through automated predictive analytics that continuously analyze virtual model data and generate maintenance recommendations without requiring constant expert intervention. The machine learning algorithms automatically identify patterns, predict failures, and suggest optimal maintenance timing, reducing the need for specialized human analysis while improving operational efficiency
Data Source
AI summary
A system for conducting a real-time power capacity assessment 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 is communicatively connected to a sensor configured to acquire real-time data output from the electrical system. The power analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a machine learning engine. The machine learning engine is configured to store and process patterns observed from the real-time data output and the predicted data output, forecasting power capacity of the electrical system subjected to a simulated contingency event.