Adaptive Power Grid Data Management for Autonomous Fault Response
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Solution Overview
Problem
Geographically distributed complex device networks, such as power grids, are susceptible to service interruptions due to external factors like weather and sabotage, and internal factors like device aging and malfunction, making it difficult to identify and rectify issues in a timely manner.
Innovation Solution
An adaptive power grid management system utilizing a network device database, a network adapter, and a processor configured to train a context model with machine learning, determine formation plans, and execute scout applications on devices to adaptively manage the network, forming self-acting operating cells that can operate autonomously and merge to achieve objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional centralized management is used for power grids, then control and coordination are simplified, but response time to failures and service interruptions increases
Solution Approach 1:
The patent divides the power grid into autonomous operating cells that can independently detect, respond to, and resolve issues. Each cell operates semi-autonomously, eliminating the need for centralized coordination for routine failures. This segmentation enables parallel processing of multiple issues simultaneously, dramatically reducing response time while maintaining manageable complexity through standardized cell architectures.
2Loss of time
If manual troubleshooting and rectification processes are used, then system complexity is reduced, but time to identify and resolve issues increases
Solution Approach 1:
Operating cells are equipped with autonomous capabilities to self-diagnose failures, self-isolate affected components, and self-rectify issues using predefined protocols and machine learning models. This self-service approach eliminates manual intervention for routine failures, reducing issue resolution time from hours to minutes while maintaining appropriate automation levels through hierarchical decision-making.
Solution Approach 2:
The system implements continuous feedback loops where operating cells monitor their own performance and the performance of neighboring cells. Real-time data from sensors, meters, and devices feeds into machine learning models that automatically adjust operations to prevent failures and optimize performance. This closed-loop feedback enables proactive issue identification and resolution before failures impact service.
3Reliability
If geographically distributed devices are monitored centrally, then data consolidation is simplified, but communication overhead and vulnerability to single points of failure increase
Solution Approach 1:
The patent implements a distributed data management architecture where each operating cell maintains its own data cache and processing capabilities. Critical data is replicated across multiple cells rather than relying on a single centralized repository. This segmentation eliminates single points of failure, as the grid can continue operating with degraded functionality even if some cells or communication channels fail, while standardised data protocols keep management complexity manageable.
Data Source
AI summary
A system for adaptive power grid management includes a data management module configured to communicate with one or more devices on the network of devices to aggregate device data from the network of devices in the asset database, receive a pov file request from one of a plurality of modules in the power grid management system, retrieve asset data from the asset database based on a content of the pov file request, select a view pattern from the patterns database based on the pov file request, form a pov file based on the asset data and the view pattern, and send the pov file to the one of the plurality of modules.