Gateway Power State Control for Critical Backup Windows
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Solution Overview
Problem
Existing UPS systems face challenges in managing power distribution during critical windows due to finite battery runtime, which can lead to suspension of backup power to critical loads, especially during temperature, age, and loading variations.
Innovation Solution
A method and system for smart power state management at network nodes, involving monitoring of power states, isolation of network devices, and adjustment of power using backup devices, with features like priority classification of loads and automatic power state management, utilizing machine-learning to predict and adjust power distribution based on network usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If battery capacity is increased to extend runtime, then backup power duration is improved, but device size and cost increase
Solution Approach 1:
The system dynamically adjusts power distribution by monitoring battery state of charge and automatically isolating non-critical loads when thresholds are approached. This dynamic load management extends effective backup duration without requiring larger battery capacity, as power is allocated based on real-time conditions rather than static design assumptions.
Solution Approach 2:
The system segments loads into critical and non-critical categories, allowing selective power distribution. By isolating non-essential devices through circuit breakers or relays when battery charge drops below thresholds, the system preserves power for essential loads, effectively extending backup duration for critical systems without increasing overall battery size.
2Reliability
If battery capacity is increased to ensure power during critical window, then reliability is improved, but device complexity increases
Solution Approach 1:
The system automatically monitors battery state of charge, compares it against predefined thresholds, and executes load isolation without user intervention. This self-service automation eliminates the need for manual power management while ensuring reliable power supply during critical windows, maintaining high reliability without proportionally increasing operational complexity.
Solution Approach 2:
The system continuously monitors battery charge levels and uses this feedback to automatically adjust power distribution. When the state of charge falls below a threshold, the system triggers isolation of non-critical loads, and when charge recovers above the threshold, it restores power. This closed-loop feedback mechanism ensures reliable power supply while automating complex decision-making processes.
3Use of energy by moving object
If load isolation is implemented to conserve power, then energy efficiency is improved, but ease of operation deteriorates
Solution Approach 1:
The system automatically performs load isolation and restoration based on battery charge levels without requiring user action. Users simply define their critical loads once, and the system handles all subsequent power management decisions autonomously, maximizing power conservation efficiency while maintaining ease of operation through automation.
Solution Approach 2:
The system introduces an automated control intermediary that mediates between battery status and load power supply. This intermediary automatically executes isolation and restoration commands based on charge thresholds, eliminating the need for users to manually manage complex power distribution while ensuring optimal energy efficiency.
Data Source
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AI summary
Methods and systems are described for power state management. A critical usage window may be configured at a gateway node. A change in a power state of the gateway node may be detected, at an interface, during the critical usage window. The power state of the gateway node may be adjusted via the interface for a set duration using a backup power node.