Virtual System Modeling for Power Distribution Optimization
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Solution Overview
Problem
Current electrical power systems lack real-time optimization capabilities due to the complexity of simulating aging effects and interdependencies, leading to inefficient management and potential false conclusions in optimizing power system operations.
Innovation Solution
A system comprising a data acquisition component, analytics server, and control element that utilizes a virtual system modeling engine to generate predicted data, monitor real-time data, and synchronize with actual system conditions, enabling real-time optimization of power resources and system control parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a rigid simulation model is used for power system optimization, then the model structure is simple and easy to implement, but it cannot reflect aging effects and system health changes over time, leading to false conclusions
Solution Approach 1:
The patent transforms the rigid, static simulation model into a dynamic virtual system model that continuously adapts to reflect aging effects and health changes. The virtual model is updated in real-time using sensor data and calibration algorithms, allowing it to dynamically adjust its parameters and predictions to match the actual evolving state of the power system, thereby maintaining reliability over time.
Solution Approach 2:
The patent implements a feedback mechanism where sensor data from the actual power system is continuously compared with predictions from the virtual model. When discrepancies exceed a threshold, calibration algorithms automatically adjust the virtual model parameters to reduce the error. This closed-loop feedback ensures the virtual model remains accurate and reflective of the actual system's aging and health status.
2Measurement precision
If real-time data acquisition and virtual model synchronization are implemented, then predictive accuracy and system optimization are improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent creates a virtual copy (digital twin) of the power system that mirrors the physical system's structure and behavior. This virtual model is populated with equivalent components and parameters, allowing accurate predictions without requiring complex real-time analysis of the actual physical system. The copy enables simplified real-time optimization while maintaining high predictive accuracy.
Solution Approach 2:
The patent performs preliminary calibration and synchronization of the virtual model with the actual system before real-time operation. Historical data and system specifications are used to pre-configur the virtual model, so that when real-time operation begins, the model is already in a accurate state. This preliminary setup reduces the computational burden during real-time operation.
3Reliability
If the virtual system model is continuously updated with real-time data through calibration, then the model remains accurate and reflective of actual system health, but additional computational resources and processing time are required
Solution Approach 1:
The patent implements periodic calibration cycles rather than continuous updating. The system monitors the divergence between virtual model predictions and actual sensor data, and only initiates calibration when the error exceeds a predefined threshold. This periodic approach maintains model accuracy while minimizing unnecessary computational overhead and processing time.
Solution Approach 2:
The patent focuses calibration efforts on adjusting only the critical parameters that have drifted from their expected values, rather than re-calibrating the entire model. By identifying and updating only the necessary parameters (such as impedance values, loss factors, or component health states), the system maintains accuracy while reducing computational burden.
Data Source
AI summary
A system for real-time optimization of power resources on an electrical system is disclosed. The system includes a data acquisition component, an analytics server, a control element 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 analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a power flow optimization engine. The virtual system modeling engine is configured to generate predicted data output for the electrical system utilizing a virtual system model of the electrical system. The control element is interfaced with an electrical system component and communicatively connected to the analytics server. The client terminal is communicatively connected to the analytics server.