Vapor Compression Control Using Phase-Based Perturbation Optimization
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Solution Overview
Problem
Conventional vapor compression systems face inefficiencies due to reliance on model-based methods that fail to accurately account for installation-specific characteristics and system variations over time, leading to suboptimal performance and increased energy consumption.
Innovation Solution
A method and system that optimize vapor compression system performance in real-time by modifying control signals with sinusoidal perturbations, determining the phase relationship between the perturbation and system response to adjust control inputs, thereby maximizing efficiency and minimizing energy consumption without relying on models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If model-based methods are used to optimize vapor compression system operation, then energy efficiency can be improved, but the system fails to account for installation-specific characteristics and system variations over time, leading to suboptimal performance
Solution Approach 1:
The system performs self-diagnosis and self-optimization by automatically detecting its own operating parameters and adjusting control signals without external intervention or pre-programmed models. The controller monitors system responses and autonomously identifies optimal operating points, allowing the system to adapt to its specific installation characteristics and temporal variations independently.
Solution Approach 2:
The system implements continuous feedback loops where the controller monitors system responses to control inputs and uses this information to adjust future control signals. By observing the actual system behavior and comparing it with expected performance, the controller adapts to installation-specific characteristics and changes over time, maintaining optimal energy efficiency without relying on pre-established models.
2Loss of energy
If mathematical models are used to predict optimal input combinations, then energy consumption can be minimized, but the models are expensive to derive and calibrate with dozens of parameters
Solution Approach 1:
The invention extracts only the essential control function needed for optimization, eliminating the need for complex mathematical models with dozens of parameters. By removing the modeling component entirely and replacing it with direct observation and adaptation, the system achieves energy minimization without the burden of complex model derivation and calibration.
Solution Approach 2:
The system replaces expensive, complex mathematical models with simple, computationally inexpensive control algorithms. Instead of investing significant resources in deriving and maintaining detailed system models, the controller uses straightforward feedback mechanisms that require minimal computational resources and can be easily implemented and adjusted.
3Ease of operation
If conventional control methods are used, then system operation is simplified, but the system cannot adapt to changes over time such as refrigerant leaks or corrosion on heat exchangers
Solution Approach 1:
The control system transitions from static, pre-programmed control to dynamic adaptation. The controller continuously adjusts control signals based on real-time system responses, allowing the system to adapt to changing conditions such as refrigerant leaks or heat exchanger corrosion while maintaining operational simplicity through automated adjustment rather than complex manual intervention.
Data Source
AI summary
A system and a method for controlling an operation of a vapor compression system are disclosed, such that a performance of the system measured in accordance with a metric of the performance is optimized. A control signal is modified with a modification signal including a perturbation signal having a first frequency, wherein the control signal controls at least one component of the vapor compression system. A metric signal representing a perturbation in the performance of the system caused by the modification signal is determined, wherein the metric signal has a second frequency substantially equal to the first frequency. The control signal is adjusted based on a function of a phase between the perturbation signal and the metric signal, such that the performance is optimized.