# A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD
## 6 Conclusions  
Due to the limitations of communication bandwidth and the Nyquist sampling theorem in data sampling and transmission, the global broadband oscillation in power systems is difficult to detect and identify. A broadband oscillation recognition method based on CS and EEMD is proposed herein. This paper focuses on the compressed sampling and reconstruction of broadband oscillation signals, as well as the EEMD decomposition of reconstructed signals. The results are as follows:  
1. In the substation, due to the limitations of communication bandwidth and transmission rate, the collected broadband oscillation signal cannot be transmitted to the main station. After the original data are converted to low-dimensional data by compressed sampling and are transmitted to the main station, the compressed sampled signal is reconstructed by the SP algorithm. The main station analyzes the reconstructed signal and can identify the original signal oscillation information. 2. EEMD is performed on the reconstructed signal to generate a set of IMFs containing false components, and IMF components containing oscillation information are screened out using the energy coefficient. Finally, the information on broadband oscillation is accurately calculated by FFT. In this paper, the method is verified by the construction signal and the simulated measurement signal. Under different SNRs, the wideband oscillating signal is compressed, sampled, and reconstructed effectively, the original oscillation information is retained, and the reconstructed signal's SNR is improved. The results show that the mentioned compressed sampling reconstruction algorithm is robust to noise and has a good denoising effect. EEMD can adaptively decompose the reconstructed signal, eliminate the false IMF component, and only retain the IMF component containing oscillation information, which shows the effectiveness and accuracy of the proposed broadband oscillation identification method. In the field of oscillation detection and its identification in power systems, it is essential to accurately identify the oscillation information, and the importance of fast calculation speed cannot be ignored. This can ensure that the power system can respond to oscillation immediately, and effectively avoid the risk of worsening the instability caused by the response delay. Conversely, if the calculation speed is slow, when the oscillation is detected and the alarm is triggered, the instability may have already spread, and the effectiveness of subsequent response measures will be greatly reduced. Therefore, in-depth exploration and the optimization of computing speed are crucial to improve the level of the power system when handling oscillation problems, which will also become a core topic in future work, to be enriched and strengthened in subsequent research. In addition, integration of the proposed method with other monitoring systems is also an important research direction, especially regarding modern intelligent monitoring systems, which often involve the fusion and real-time processing of multiple sensor data. The potential for integration with existing monitoring systems can be considered in terms of data compatibility and system integration. In terms of data compatibility, the proposed signal reconstruction technology can remove noise, improve signal quality, and significantly improve data availability when combined with multiple sensors. In terms of system integration, the monitoring system needs multi-source data fusion and collaboration. This technology can process and integrate signals from different devices or sensors to improve global monitoring capabilities, especially in terms of integrating with large-scale monitoring systems. This method can achieve good compression and the reconstruction of big data. These are also problems that need further study. Conceptualization, J.G. ; Methodology, J.G. and H.X. ; Software, H.X. and Y.Y. ; Validation, Y.Y. ; Formal analysis, J.G. ; Investigation, H.X. and Y.Y. ; Resources, J.G. ; Data curation, J.G.