Air Conditioning Load Learning for Compressor Coordination
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Solution Overview
Problem
Large air conditioning systems face challenges in determining the supply and demand relationship between parallel components and coordinating them for stable on-demand cooling/heating and improved energy efficiency, especially when only part of the indoor heat exchange units are in use.
Innovation Solution
A control method that acquires actual cooling/heating capacity and temperature change rates to learn the heat exchange load characteristic curve, allowing for adjustment of compressor numbers and rotational speeds, or injector operation based on steady state and desired loads to ensure efficient operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If multiple parallel compressors and injectors are used to improve partial-load regulation ability, then energy efficiency is improved, but device complexity increases
Solution Approach 1:
The air conditioning system divides the compression and injection functions into multiple parallel compressors and injectors, allowing independent operation and optimization of each component. This segmentation enables the system to select and operate only the necessary number of compressors/injectors based on load requirements, improving energy efficiency while managing complexity through modular architecture
Solution Approach 2:
The system dynamically adjusts the number of operating compressors and injectors based on real-time load conditions and learned heat exchange characteristics. The coordination control module continuously optimizes which components are active and at what capacity levels, enabling adaptive energy efficiency improvements without requiring permanent complex configurations for all possible operating scenarios
2Measurement precision
If automatic learning of heat exchange load characteristic curve is implemented to improve coordination accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system implements automatic learning by continuously monitoring actual cooling/heating capacity output and indoor temperature change rates, then using this feedback to build and refine heat exchange load characteristic curves. This feedback mechanism improves measurement precision and coordination accuracy by adapting to actual system behavior without requiring overly complex predetermined control algorithms
Solution Approach 2:
The coordination control module performs self-learning and self-optimization by automatically analyzing operational data and adjusting compressor/injector coordination strategies. This self-service capability improves measurement and control precision while avoiding the need for external complex control systems, as the system learns and adapts autonomously
Data Source
AI summary
A control method for an air conditioning system. The control method includes: S100, acquiring an actual cooling/heating capacity output by the air conditioning system, and acquiring an actual temperature change rate of an indoor heat exchange unit; S200, automatically learning a heat exchange load characteristic curve of the indoor heat exchange unit based on the actual cooling/heating capacity and the temperature change rate; S300, acquiring a steady state load and/or a desired load of the indoor heat exchange unit based on the heat exchange load characteristic curve; and S400 adjusting the number of operating compressors and rotational speeds of compressors, and/or adjusting the number of operating injectors and opening degrees of injectors, based on the steady state load and/or the desired load.