# Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method
## 6 Conclusions  
In this paper, a new method based on the IA-VMD method and a bandpass filter is proposed to identify broadband oscillation signals. This method can solve problems (including mode aliasing and noise effect) that cannot be solved by the traditional signal identification method, and it also meets the requirements of modal identification. This method can identify the LFO interval mode, the LFO local mode, subsynchronous oscillation, and supersynchronous oscillation from broadband signals. In addition, this method can identify not only the modes of obvious oscillation signals after a disturbance but also ambient signals before the disturbance. It can also identify the oscillation characteristic parameters of potential oscillation from ambient signals, and the parameters provide a reference for the early warning of oscillations of the power system.  
In this paper, the proposed method was used to identify obvious oscillation signals after a disturbance and ambient signals before the disturbance, and the results were compared. By comparing the identification results of the two types of signals, it was found that the two frequencies were very close to each other. Therefore, it is further verified that the new method proposed in this paper has good application in mode identification and early warning of system oscillation.  
Methodology, F.L.; Supervision, K.L. and D.S.; Validation, R.Z.; Writing--original draft, S.L.; Writing--review & editing, C.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China under Grant 61973318, the Distinguished Young Foundation of Hunan Province of China under Grant 2020 JJ2045, the Key R&D Program of Hunan Province of China under Project 2020 WK2007, and the Fundamental Research Funds for the Central Universities of Central South University 1053320190264. [PERSON] was supported by the base part of the Government Assignment for Scientific Research from the Ministry of Science and Higher Education of Russia, project code: FZZS-2020-0039.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy reason.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method and IMF**} & \multicolumn{3}{c}{**IA-VMD-Prony**} & \multicolumn{3}{c}{**EMD-Prony**} & \multicolumn{3}{c}{**MF-Prony**} \\ \cline{2-11}  & **1** & **2** & **3** & **4** & **1** & **2** & **3** & **1** & **2** & **3** \\ \hline \(\mathrm{Frequency}/\mathrm{Hz}\) & **1.47** & 1.78 & 3.59 & 0.32 & **1.55** & 1.29 & 0.82 & **3.74** & 1.57 & 35.2 \\ Amplitude/MW & **4.82** & 1.56 & 1.32 & 0.43 & **5.93** & 1.44 & 0.97 & **7.90** & 5.54 & 3.72 \\ Damping Ratio & \(\mathbf{-0.064}\) & \(-0.26\) & \(-0.23\) & \(-0.13\) & \(\mathbf{-0.29}\) & \(-3.11\) & \(0.035\) & \(\mathbf{-0.016}\) & \(-0.74\) & \(-5.9\) \\ \hline \hline \end{tabular} The dominant oscillation mode is indicated in the bold part.  
\end{table}
Table 3: Modal identification of the ambient signal.