# A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD
###### Abstract
However, the traditional PMU mainly detects oscillation signals in the range of 45-55 Hz near the power frequency [13]; thus, the traditional PMU cannot meet the requirements of the multi-mode and high-frequency detection of broadband oscillation. A broadband oscillation detection method based on the main station is used to centrally analyze the synchronous phasor data uploaded by PMU substations in the whole network and can detect the propagation and development process of broadband oscillation from a global perspective. Although the broadband oscillation method based on a main station is a popular detection method at present, it is limited by the communication bandwidth and the Nyquist sampling theorem, the main station can only monitor and analyze part of the sub-synchronous oscillation signal with a power frequency below 50 Hz, and it is difficult to effectively monitor the whole wideband. In Ref. [14], autoencoders are used to encode and compress PMU data in the substations of a power system and then identify sub-synchronous oscillations. Although the problem of massive data transmission by communication lines is solved, the existing PMU devices need to be improved. In Ref. [15], an adaptive parameter method using a band-pass filter was used to identify the oscillation information of different frequency bands but did not consider robustness to noise. In Ref. [16], an adaptive variational mode decomposition (VMD) method was used to realize the modal identification of broadband oscillation, composed of five signals with different frequencies in a white-noise environment with an amplitude of 0.2, but this method did not include the sampling and processing of high-frequency signals. In Ref. [17], TimesNet and an Autoformer algorithm were combined for dimensionality reduction and the reconstruction of high-dimensional data obtained after high-frequency sampling. The reconstructed signal after dimensionality reduction was processed by convolutional neural network (CNN) technology with deep learning to realize the recognition and location of wide-band oscillation, but it ignores the problems of CNN, such as high data requirements, making it easy to overfit when training data are insufficient. In Ref. [18], particle swarm optimization (PSO) was applied to calculate the parameters of the VMD algorithm to process broadband oscillation signals; this method has good robustness to noise, but there is no discussion on how to collect and process high-frequency signals. Therefore, when using phasor data in a WAMS main station to detect broadband oscillation, firstly, the problem of data transmission must be solved; namely, how to make the data in the substation efficiently transmitted to the master station. Secondly, it is necessary to decompose the broadband oscillation signal effectively in the main station to remove irrelevant and redundant information. Finally, information on oscillation modes is identified simply and effectively. To address the difficulty of detecting broadband oscillation in a power system, a multi-mode recognition method for broadband oscillation based on compressed sensing subspace pursuit (CS-SP) and ensemble empirical mode decomposition (EEMD) is put forward in this paper. Firstly, we selected a Gaussian random sampling matrix to compress the broadband oscillation data, which overcomes the problem of [PERSON]'s theorem and insufficient data sampling and the transmission of data in the communication bandwidth. Secondly, after the main station received the low-dimensional oscillation data, the subspace pursuit (SP) algorithm was used to reconstruct the compressed low-dimensional data, and the original signal was largely recovered. Finally, the recovery signal was decomposed by EEMD, and a set of the intrinsic mode functions (IMFs) of the oscillating signal were obtained. We calculated each IMF energy coefficient and the excess IMF components were removed.