Digital Twin for Intelligent Electronic Device Configuration
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Solution Overview
Problem
The complexity and scale of substation automation systems require more efficient methods for configuring intelligent electronic devices (IEDs) to manage increasing parameters, dependencies, and dynamic behavior, especially in self-healing and self-adapting scenarios, while optimizing the use of operational and non-operational data for improved performance and fault resolution.
Innovation Solution
A method involving a virtual model, or digital twin, hosted in a computing system that receives electrical signals and configuration parameters to determine performance parameters, adapt the model, and transmit updated functional parameters to configure IEDs, utilizing AI and ML for adaptive behavior and self-healing capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional model-based or hand-coded methods are used to develop application functions, then the system can be configured using standard literature, but the system complexity increases and adaptability decreases with growing scale and parameters
Solution Approach 1:
The patent creates a virtual copy (digital twin) of the physical substation automation system that mirrors its structure, parameters, and behavior. This virtual model can be configured and tested independently, then deployed to the physical system, reducing configuration complexity while maintaining accuracy through the copying relationship between virtual and physical systems.
Solution Approach 2:
The digital twin allows configuration, testing, and validation of application functions to be performed in advance on the virtual model before deployment to the physical system. This preliminary action on the copy enables thorough testing and optimization without affecting the actual operational system, reducing on-site configuration complexity.
2Adaptability or versatility
If more parameters and functions are added to handle increasing complexity, then the system can manage more scenarios, but the difficulty of detecting and measuring system state increases
Solution Approach 1:
The digital twin creates a complete virtual replica of the physical system with all its parameters and state variables. This copy allows comprehensive monitoring and analysis of system state in the virtual domain, making it easier to detect and measure complex system states without directly complicating the physical measurement infrastructure.
Solution Approach 2:
The digital twin acts as an intermediary between the physical system and the monitoring/analysis tools. By interfacing with the virtual model rather than directly with the complex physical system, operators can more easily detect and measure system state through the simplified virtual representation while maintaining full system adaptability.
3Ease of operation
If manual configuration and fault resolution methods are used, then the system can be controlled, but the loss of time for fault resolution increases and reliability decreases
Solution Approach 1:
The digital twin provides a virtual copy of the system that can be used for rapid fault analysis and resolution planning without affecting the physical system. Operators can diagnose and resolve faults in the virtual model first, then apply solutions to the physical system, significantly reducing actual fault resolution time while maintaining operational control.
Solution Approach 2:
The system enables preliminary fault detection, analysis, and resolution planning to be performed in advance on the digital twin. By preparing fault resolution strategies in the virtual environment before they are needed in the physical system, the actual fault resolution time is reduced while operators maintain full control over the resolution process.
4Reliability
If self-healing and self-adapting capabilities are implemented, then system reliability increases, but the device complexity increases
Solution Approach 1:
The digital twin serves as a virtual platform where self-healing and self-adapting algorithms can be developed, tested, and validated before deployment to the physical system. This copying approach enables advanced reliability features to be implemented while containing complexity in the virtual domain, reducing the complexity burden on the physical system.
Solution Approach 2:
The system implements self-service capabilities where the digital twin autonomously performs monitoring, diagnosis, and optimization functions. The virtual model can automatically detect issues, analyze root causes, and apply corrections without human intervention, increasing reliability while managing complexity through automation rather than human expertise.
Data Source
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AI summary
The present invention relates to a method for configuring an intelligent electronic device (IED). The IED comprising an application function and is connected to a virtual model hosted on a computing system, the virtual model comprises a virtual application function corresponding to the application function of the IED, the method comprising: receiving measured electrical signals, and configuration parameters from the IED; determining a first performance parameter for the virtual application function; adapting the virtual model by updating functional parameters of the virtual application function; determining a second performance parameter for the virtual application function using the adapted virtual model; comparing the difference between the first performance parameter and the second performance parameter to determine an improvement in the virtual model with a threshold; transmitting the functional parameters to configure the application function of the IED based on the difference between the first performance parameter and the second performance parameter.