## 5 Conclusions  
In this paper, a novel current sharing detection method based on LSTM is proposed for parallel-connected SMPS system. The method is based on the neural network structure of CNN + LSTM to identify the subtle differences of each branch's RMBS in the ripple spectrum.In addition, a sliding window algorithm is added to estimate the actual output power of each SMPS. The method's feasibility is verified through the experiments and the estimation accuracy is acceptable for application. Compared with traditional methods, the most prominent advantage is that the posted method only needs to detect the ripple voltage at the load end to detect the current sharing state of the parallel redundant system, and the actual output power and current of each branch can be estimated. Therefore, this method has the advantages of low cost, non-invasiveness, and easy implementation. At the same time, there are two main problems in practical applications worthy of further research. 1: The posted method cannot currently distinguish CCM and DCM mode of SMPSs. That is to say, its application needs to be established on the premise that all SMPSs work in a single certain mode. This limitation reduces the application value of the posted method. 2: Our experiments are based on resistors, and the interference factors of the same frequency band introduced by the load terminal are not considered. It will be a huge challenge in practical applications. Figure 9: The current Sharing state estimation result. **Author Contributions:** Conceptualization, P.H. and Q.Z. ; methodology, L.B. ; software, S.X. ; validation, W.Z. ; formal analysis, P.H. ; investigation, P.H. ; resources, S.X. ; data curation, S.X. ; writing--original draft preparation, P.H. ; writing--review and editing, Q.Z. ; visualization, S.X. ; supervision, L.B. ; project administration, L.B. ; funding acquisition, L.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the National Natural Science Foundation of China under Grants No. U1830133 (NSFC) and the Project of Sichuan Youth Science and Technology Innovation Team, China (Grant No. 2020 JDTD0008). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**  
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