UPS Predictive Load Compensation for Real-Time Power Conditioning
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Solution Overview
Problem
Traditional UPS systems are inefficient in managing transient events and dynamic load requirements, relying on reactive power conditioning and lacking predictive capabilities, leading to inefficiencies and increased operational costs.
Innovation Solution
A predictive load compensation system using analog twinning and real-time telemetry feedback, integrating a bidirectional high-discharge battery and TensorFlow processing, enables adaptive power conditioning by preemptively aligning power delivery with load demands, performing micro-adjustments at sub-millisecond intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional reactive power conditioning is used in UPS systems, then the system structure remains simple, but the system cannot predict or preemptively respond to transient events and dynamic load requirements, leading to inefficiencies and increased operational costs
Solution Approach 1:
The patent implements predictive load compensation by analyzing historical load data and system responses to preemptively adjust power delivery before transient events occur. The system uses machine learning models to forecast load requirements and proactively conditions power to match predicted demands, eliminating the need for reactive corrections and improving system adaptability without proportionally increasing complexity
Solution Approach 2:
The system continuously monitors actual load responses and compares them with predicted values, using this feedback to refine predictive models and adjust power conditioning parameters in real-time. This closed-loop feedback mechanism enables the system to learn from past performance and improve predictive accuracy, enhancing adaptability while maintaining manageable complexity through iterative optimization
2Measurement precision
If computational algorithms are made more intensive to improve load prediction accuracy, then prediction precision improves, but system response time decreases due to processing delays
Solution Approach 1:
The patent divides the computational workload into segmented processing stages: real-time feature extraction from sensor data, intermediate prediction calculations using simplified models, and periodic refinement using more intensive machine learning algorithms. This segmentation allows the system to maintain fast response times for critical functions while periodically improving prediction accuracy through more computationally intensive analysis
Solution Approach 2:
The system applies partial computational action by using lightweight predictive models for immediate response and reserving intensive computational resources for periodic model refinement and long-term pattern analysis. This approach ensures that critical real-time predictions are made quickly with sufficient accuracy, while more intensive calculations are performed asynchronously to enhance overall prediction precision without compromising response time
3Productivity
If discrete isolated systems operate without predictive analytics, then system complexity remains low, but energy optimization and operational efficiency are reduced
Solution Approach 1:
The patent integrates predictive analytics, real-time monitoring, and power conditioning control into a unified system architecture. By merging these previously discrete functions into a coordinated system that shares data and control mechanisms, the patent achieves improved operational efficiency through proactive optimization while managing integration complexity through modular design and standardized interfaces
Data Source
AI summary
A method for conditioning and maintaining power with predictive load compensation using an uninterruptible power supply system is disclosed. The method includes measuring an electrical input from a primary power supply using analog sensors and transmitting the input through an impedance to introduce a controlled delay. During the delay, input parameters and downstream system effects are determined based on telemetry sensor data. Digital identities are generated for the electrical input and the downstream load and compared to a stored digital identity representing historical conditions. Using the comparison, a system simulation is performed with analog twinning to predict downstream system responses. The predicted response is used to adjust a power converter parameter, modifying the electrical input to an intermediate output that compensates for voltage fluctuations and harmonic distortions. A secondary power supply with a bidirectional high-discharge battery supplements or stores energy as needed, ensuring stable power delivery to the downstream load.