Backup Generator Control in Communications Enclosures Using ML
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Solution Overview
Problem
Existing backup power systems for outdoor applications, such as telecommunications, lack efficient control and configuration methods to optimize generator performance and reduce downtime during power outages.
Innovation Solution
A communications enclosure with a generator module and a control module that utilizes a machine learning model to process input variables and generate outputs for controlling the generator, along with a configurator to create a graphical representation and bill of materials for backup system configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional control methods are used for generators in backup power systems, then the system structure is simple, but generator performance cannot be optimized and downtime cannot be reduced
Solution Approach 1:
The machine learning model enables the generator control system to automatically optimize its own performance by processing input variables and generating control outputs without human intervention. The system self-adjusts to optimize generator performance and reduce downtime during power outages
Solution Approach 2:
Traditional mechanical control methods are replaced with an intelligent machine learning-based control system. The ML model processes operational data and generates optimized control signals, substituting conventional control mechanisms with data-driven intelligence
2Ease of manufacture
If manual configuration methods are used for backup systems, then the configuration process is straightforward, but system setup time and complexity increase
Solution Approach 1:
Manual configuration processes are replaced with automated machine learning-based configuration. The system automatically processes system parameters and generates optimized configurations, eliminating time-consuming manual setup while improving configuration accuracy
Solution Approach 2:
The machine learning model uses historical operational data and system parameters as inputs to generate optimized control strategies and configurations. By learning from past performance data, the system creates optimized configurations without requiring manual reconfiguration
Data Source
AI summary
Aspect of the invention relate to apparatuses and methods for backup power systems. The apparatus comprises a communications enclosure. The communications enclosure comprises a generator module. The generator module comprises a generator and a rack mount.