# Synthetical Modal Parameters Identification Method of Damped Oscillation Signals in Power System
## 6 Conclusions  
To solve the problems of insufficient precision and parameters in modal identification of damped low-frequency oscillation (DLFO), a comprehensive method, called synthetical modal parameters identification (SMPI) method, is presented by integrating the advantages of empirical mode decomposition (EMD), stochastic subspace identification (SSI) and Prony algorithm assisted by parameter matching. In the SMPI method, the DLFO signals are, firstly, denoised by EMD. Afterward, the Prony and SSI approaches are adopted to identify the modal parameters of the filtered signals after EMD, respectively. Lastly, the proposed parameters matching method is developed to match the accurate modal parameters of DLFO in line with the similar frequencies recognized by SSI and Prony, and avoid the difficulty of estimation of model order. The proposed SMPI method is demonstrated by ideal simulated signals with known modal parameters and real-time signals from power system case studies. The primary conclusions are as follows:  
Figure 19: Comparison of \(Y\) and \(c_{EMD}(t)\) of the real-time signal.  
Figure 18: Dominant components of real-time signal identified by SMPI.  
1. To solve the problem that SSI and Prony are both sensitive to noise, EMD shows the potential to denoise the original DLFO signals and cut down the occurrence of fictitious modes to some extent, enhancing the accuracy of modal identification;
2. Integrating the strengths of Prony and SSI by the parameter matching method is capable of purchasing precise and full modal parameters of DLFO, and handily avoiding the difficulty of estimating model orders in Prony and SSI; and
3. Through the case studies on simulated signals with known modal parameters and real-time signals from some power systems, it is demonstrated that SMPI holds satisfactory precision, robustness, and generalization ability.  
This study on the proposed SMPI method is conducive to improve the stability of the power system by controlling and even avoiding the oscillation in time. Furthermore, SMPI shows the potential for prognostics and health management in different conditions and fields, such as construction, aeronautics and marine for its robustness and generalization ability.  
Conceptualization, H.L. and S.B.; Funding acquisition, C.-W.F.; Investigation, H.L., S.B. and J.-R.W.; Methodology, H.L., S.B. and C.-W.F.; Validation, H.L.; Writing--original draft, H.L.; Writing--review and editing, C.-W.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51975124; and the Shanghai Belt and Road International Cooperation Project of China, grant number 20110741700.  
Not applicable.  
The data used to support the findings of this study are included within the article.  
This paper was co-supported by the National Natural Science Foundation of China (Grant No. 51975124) and the Shanghai Belt and Road International Cooperation Project of China (Grant No.20110741700), and support from Hong Kong Research Grant Council for the Research Project (15200418). All authors would like to thank them.  
The authors declare no conflict of interest in publication.