An improved Rényi entropy method for centrifugal compressor surge warning

Centrifugal compressors are critical components in diverse power systems. However, surge phenomena pose severe risks to both the thermodynamic stability of the cycle and the mechanical integrity of the compressor. Early and accurate detection of surge inception is therefore essential for effective control and protection. This study introduces a novel surge precursor identification method based on Rényi entropy analysis of acoustic emissions. Experiments were performed on an automotive turbocharger compressor, with four microphones mounted upstream of the inlet to record acoustic signals during transient operation approaching the surge limit. Conventional Rényi entropy was found to be insufficiently sensitive to surge onset due to overfitting. To overcome this limitation, an improved Rényi entropy method was developed by reconstructing the probability function. The improved entropy exhibited a clear increasing trend prior to surge inception, effectively indicating the compressor’s approach to instability. Comparative analyses demonstrate that the improved Rényi entropy surpasses conventional entropy-based indicators—such as approximate, sample, and fuzzy entropy—in both sensitivity and robustness. To verify the general applicability of the approach, a second experiment was conducted on a fuel cell centrifugal compressor equipped with 13 inlet microphones. The improved Rényi entropy method consistently detected surge inception in this configuration as well, confirming its reliability across different compressor types. These findings highlight the potential of the improved Rényi entropy as a powerful early-warning metric for surge detection in centrifugal compressors, marking an important step toward noise-based instability monitoring and control.