Fusion Deconvolution for Reliability Analysis of A Flywheel-Battery Hybrid Energy Storage System

Abstract

A hybrid flywheel-battery energy storage system is able to smooth the battery charging/discharging; harmful impact can be filtered by the flywheel to reduce battery damage and extend battery life. However, due to extremely high rotating speed of the flywheel, the hybrid storage system is often subject to mechanical failures in the flywheel transmission system. Therefore, it is critical to detect unexpected faults in the flywheel transmission to ensure the normal operation of the hybrid energy storage system. To this end, in this study a new fusion deconvolution is proposed to perform reliability analysis on the hybrid flywheel-battery energy storage system. Firstly, the Deterministic Random Separation (DRS) is used to simplify the sensory signal components to obtain the fault impulse. Then, the multipoint kurtosis (MKurt) of the impulse is used to determine the parameters of the multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) model. Lastly, the envelope analysis of the signal filtered by the MOMEDA deconvolution is carried out to obtain the characteristics of the periodic fault pulses. Both numerical and experimental results demonstrate that the proposed fusion deconvolution method produces satisfactory diagnostic performance. Compared with existing popular deconvolution algorithms, the proposed method proposed improves the extraction of the periodic fault pulses.