Research on the dynamic characterization and detection of refrigerant leakage in multi-connected air-conditioning system

Abstract

Microleakage, frequently arising from aging or inadequate installation, significantly affects the performance and longevity of refrigeration systems. The presence of microleakages, while not precipitating an immediate system failure, results in the gradual deterioration of components and escalates the energy consumption within buildings. Nonetheless, identifying these microleakages through conventional methodologies poses significant challenges. This study introduced a dynamic model specifically designed for detecting microleakages in multi-connected air conditioning systems, particularly in variable refrigerant volume (VRV) systems. Experiments performed under standard operating conditions were integral to enhancing the model's accuracy and furthering its validation. The model integrates the structural and thermophysical attributes of various components, thereby addressing the issues related to data acquisition in standard VRV during instances of microleakage. The analysis of dynamic parameter shifts during system microleakage led to the generation of a comprehensive dataset, which proves invaluable for machine learning and fault detection tasks. A detection and alerting protocol is written in Python presented, chiefly anchored on pressure deviations, augmented by temperature shifts and refrigeration and heating capacities, all represented as virtual volumetric alterations. The findings indicate a delayed transmission of the leakage signal from the leakage point to the system's detection points, with the maximal delay occurring at the compressor inlet under refrigeration conditions, amounting to 239 s. Evaluation under prescribed conditions indicated variances in pressure, temperature, and refrigeration and heating capacities by up to 13.2 %, thereby validating the model's precision and reliability. The program, tested with the generated dataset, accurately identifies leakage states within an average time of 20 min. This model and detection strategy provide a new perspective on microleakage in refrigeration systems, suggesting a robust strategy for their sustained and secure operation.