Preplanned Macrosite Swapping for Outage Power Management
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Solution Overview
Problem
Existing telecommunications networks face inefficiencies in power management during power outages, as manual intervention is labor-intensive, prone to errors, and fails to optimize power distribution across the network effectively.
Innovation Solution
A cloud-based observability framework (OBF) with artificial intelligence and machine learning is implemented to detect power outages, activate auxiliary power sources at selected macrosites, and temporarily disable non-essential macrosites, optimizing power distribution using kubernetes clusters and virtualized units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual power management is used during power outages, then human operators can respond to alerts, but the system becomes labor-intensive, error-prone, and slow in optimizing power distribution
Solution Approach 1:
The system enables self-service through automated detection of power outage events, automatic identification of affected macrosites, and autonomous activation of auxiliary power sources. The cloud-based platform continuously monitors network status and triggers power management actions without human intervention, allowing the system to manage itself during outage events
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring power status at macrosites and adjusting power distribution based on real-time conditions. The cloud-based platform receives status updates from macrosites and automatically modifies power allocation to optimize distribution during outages, creating a closed-loop control system that adapts to changing conditions
2Reliability
If auxiliary power sources are activated at all macrosites, then complete network coverage is maintained, but power consumption and cost increase significantly
Solution Approach 1:
The system applies local quality by selectively activating auxiliary power sources only at specific macrosites that are determined to be critical for maintaining network coverage. Rather than uniformly powering all macrosites, the system identifies and prioritizes specific locations based on their importance to overall network functionality, thereby reducing total power consumption while maintaining essential coverage
Solution Approach 2:
The system employs partial action by activating auxiliary power sources at a subset of macrosites rather than all macrosites. The cloud-based platform determines the minimum necessary number of macrosites required to maintain acceptable network coverage during outages, activating power sources only where absolutely necessary to sustain critical services
3Productivity
If a predetermined number of macrosites are selected for auxiliary power, then power distribution is optimized, but the selection process becomes complex
Solution Approach 1:
The system extracts the complex selection logic from manual processes and embeds it within the cloud-based platform's automated algorithms. The platform automatically evaluates macrosite importance, predicts outage impact, and determines optimal auxiliary power allocation using pre-configured criteria and real-time data, removing the complexity burden from operators while maintaining sophisticated selection capabilities
Solution Approach 2:
The system performs preliminary action by pre-configuring selection criteria and importance weights for macrosites before outages occur. The cloud-based platform maintains databases of macrosite characteristics, historical performance data, and priority classifications that are prepared in advance, enabling rapid automated decision-making during outages without requiring complex real-time analysis
Data Source
AI summary
A system and method that detects initiation of a triggering event associated with a coverage area having a plurality of macrosites. The system and method further activate an auxiliary power source at each of a predetermined number of selected macrosites, the selected macrosites being ones of the plurality of macrosites. A number of the plurality of macrosites is greater than the predetermined number of selected macrosites. The system and method further include temporarily disabling at least one macrosite other than the selected macrosites.