Virtual Model Calibration for DC Microgrid Power Analytics
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Solution Overview
Problem
Current technologies lack real-time power analytics capabilities for DC microgrids in mission-critical power systems, leading to inaccurate reliability predictions and increased operational costs due to the inability to synchronize with actual system conditions and age with the facility, resulting in inefficient management and potential failures.
Innovation Solution
A system comprising a processor, memory, and display device configured with a component database, control engine, and topology modeling engine, which generates predicted data and performs real-time analytics by calibrating and synchronizing a virtual system model with actual data from external sources to provide accurate health and performance insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time analytics and monitoring are implemented for DC microgrids, then system reliability and operational efficiency are improved, but device complexity and implementation costs increase
Solution Approach 1:
The system is divided into distinct functional modules: data acquisition module, virtual system model module, analytics engine module, and user interface module. Each module performs a specific function, making the overall complex system manageable and maintainable while achieving real-time monitoring and analytics capabilities for DC microgrids.
Solution Approach 2:
A virtual system model acts as an intermediary between the physical DC microgrid and the analytics engine. This virtual model synchronizes with actual system conditions and serves as a bridge for real-time data processing and analysis, enabling reliable predictions without directly complicating the physical system architecture.
2Measurement precision
If real-time data processing and synchronization are performed continuously, then measurement precision and prediction accuracy are improved, but energy consumption and computational resources increase
Solution Approach 1:
The system performs calibration and synchronization operations at periodic intervals rather than continuously. The analytics engine periodically compares virtual system model data with actual real-time data from external sources, maintaining prediction accuracy while reducing unnecessary computational resource consumption during stable operating conditions.
Solution Approach 2:
The virtual system model automatically synchronizes with actual system conditions by receiving real-time data from external sources and self-calibrating when discrepancies are detected. This self-service mechanism maintains measurement precision without requiring constant external intervention or excessive computational resources.
3Reliability
If the system ages and synchronizes with actual facility conditions in real-time, then reliability predictions become more accurate, but system complexity and data processing requirements increase
Solution Approach 1:
A virtual system model is created as a digital copy of the physical DC microgrid. This virtual model ages and synchronizes with actual facility conditions by receiving real-time data from external sources, enabling accurate reliability predictions without requiring complex processing of all raw sensor data. The virtual model maintains the essential system characteristics while simplifying data analysis.
Data Source
AI summary
Systems and methods for performing power analytics on a microgrid. In an embodiment, predicted data is generated for the microgrid utilizing a virtual system model of the microgrid, which comprises a virtual representation of a topology of the microgrid. Real-time data is received via a portal from at least one external data source. If the difference between the real-time data and the predicted data exceeds a threshold, a calibration and synchronization operation is initiated to update the virtual system model in real-time. Power analytics may be performed on the virtual system model to generate analytical data, which can be returned via the portal.