调制方式的优化方法、控制器、电子设备及制冷设备
By acquiring sample operating data of refrigerator compressors under different operating conditions, the optimal modulation strategy was selected based on dual optimization objectives. A multi-dimensional feature dataset was constructed and a mapping model was trained. This solved the problem of the limited adaptability of existing refrigerator compressor control strategies, realized adaptive optimization of the compressor, and improved energy efficiency and operational reliability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- MIDEA BIOMEDICAL CO LTD
- Filing Date
- 2026-05-09
- Publication Date
- 2026-07-17
AI Technical Summary
Existing refrigerator compressor control strategies rely on a single parameter and human experience when switching modulation modes, resulting in a limited range of adaptability. They cannot achieve the comprehensive optimization of three-phase current waveform and system energy consumption across the entire operating range, thus limiting the improvement of the compressor's overall energy efficiency and operational adaptability.
By acquiring multiple sets of sample operating data of the compressor under different operating conditions, the optimal modulation strategy is selected based on the dual optimization objectives of system power loss and output current harmonic content. A multidimensional feature dataset is constructed, parameter correlation is analyzed, a target mapping model is trained and embedded into the control system to achieve adaptive control of the compressor.
It enhances the adaptability and long-term operational reliability of the refrigerator compressor, optimizes system energy efficiency and current waveform quality, breaks through the limitations of traditional methods, and realizes dynamic adjustment of modulation strategy.
Smart Images
Figure CN122149120B_ABST