Intelligent Network Framework for Power Grid Management
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Solution Overview
Problem
The management of power grids and rail networks is inefficient due to outdated technologies, leading to underutilization of capacity and high operational costs, with existing digital upgrades not fully addressing the need for improved monitoring and control systems.
Innovation Solution
An intelligent network framework that includes endpoint sensors, infrastructure sensors, and a network core for data collection and analytics, enabling real-time monitoring and decision-making across the system, with buses for data transmission and enterprise systems for centralized control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional management methods are used for power grids and rail networks, then operational simplicity is maintained, but management efficiency and resource utilization are poor
Solution Approach 1:
The system segments the network into multiple zones with distributed control units, each managing local operations independently. This segmentation allows complex management tasks to be distributed across multiple simple units, improving overall management efficiency without requiring a single complex centralized system.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor network conditions, control units process this information, and automatically adjust operations. This feedback mechanism enables efficient resource allocation and fault detection, significantly improving management efficiency through real-time data-driven decisions.
2Measurement precision
If more sensors and monitoring devices are deployed, then data collection capability is improved, but system complexity and cost increase
Solution Approach 1:
The system employs universal sensor units that can detect multiple types of parameters (temperature, vibration, humidity, etc.) using the same hardware platform. This multi-functionality approach achieves comprehensive monitoring accuracy without proportionally increasing system complexity, as single sensor units replace multiple specialized sensors.
Solution Approach 2:
The system introduces intelligent edge computing units that act as intermediaries between sensors and the central control system. These edge units pre-process and filter sensor data locally, reducing the complexity of data transmission and processing in the central system while maintaining high monitoring accuracy through localized intelligence.
3Speed
If real-time data processing is implemented, then decision-making speed is improved, but computational resource requirements and system complexity increase
Solution Approach 1:
The system divides computational tasks into segments distributed across edge computing units and centralized control systems. Simple real-time processing is performed locally at the edge to achieve fast decision-making for critical operations, while complex analytical processing is handled centrally, balancing speed requirements with processing complexity.
Solution Approach 2:
The system implements partial real-time processing by prioritizing critical data streams for immediate processing while allowing non-critical data to be processed with lower urgency. This approach achieves decision-making speed for essential operations without requiring excessive computational resources for all data types simultaneously.
Data Source
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AI summary
A network intelligence system may include a plurality of sensors located throughout and industry system. The sensors may obtain data related to various aspects of the industry network. The network intelligence system may include system endpoint intelligence and system infrastructure intelligence. The system endpoint and system infrastructure intelligence may provide distributed intelligence allowing localized decision-making to be made within the industry system based in response to system operation and occurrences. The network intelligence may include a centralized intelligence portion to communicate with endpoint and infrastructure intelligence. The centralized intelligence portion may provide responses on a localized level of the system or on a system- wide level.