# A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD
###### Abstract  
In power systems, the application of wind power generation equipment and power electronic devices leads to an increased frequency of broadband oscillation events, and the detection of oscillation information becomes extremely difficult, due to the limitations of communication bandwidth and the sampling theorem. To ensure the safety and stability of a power system, this paper presents a new recognition method of broadband oscillation information, which combines compressed sensing (CS) technology and an ensemble empirical mode decomposition (EEMD) algorithm to solve the problem of wideband oscillation recognition. Firstly, the broadband oscillation signal data collected by the phasor measuring unit (PMU) is compressed and sampled by a Gaussian random matrix in the substation, then the low-dimensional data obtained is uploaded to the main station. Secondly, in the main station, the subspace pursuit (SP) algorithm is used to reconstruct the low-dimensional signal; the broadband oscillation signal is recovered without losing the main features of the signal. Finally, we use the EEMD algorithm to decompose the reconstructed signal; the intrinsic mode function (IMF) components containing wideband oscillation information are screened by the energy coefficient, and the wideband oscillation information is identified. ensemble empirical mode decomposition; broadband oscillation; modal identification; compressed sensing; subspace pursuit +
Footnote †: journal: Applied sciences  
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Footnote 1: State Grid Gansu Electric Power Research Institute, Lanzhou 730217, China; [EMAIL_ADDRESS] (J.G. ); [EMAIL_ADDRESS] (Y.Y. ); [EMAIL_ADDRESS] (H.N. )1 State Grid Gansu Electric Power Company, Lanzhou 730070, China; [EMAIL_ADDRESS] 1  
Footnote 1: State Grid Gansu Electric Power Company, Lanzhou 730217, China; [EMAIL_ADDRESS] (J.G. ); [EMAIL_ADDRESS] (J.G. ); [EMAIL_ADDRESS] (H.N. )1 State Grid Gansu Electric Power Company, Lanzhou 730070, China; [EMAIL_ADDRESS] 1  
Footnote 1: State Grid Gansu Electric Power Company, Lanzhou 730217, China; [EMAIL_ADDRESS] (J.  
multi-mode capability, and the causes are complex. In terms of oscillation mechanism, traditional oscillations mainly include low-frequency oscillations led by a generator rotor with generator effect and sub-synchronous resonance/oscillation, led by shafting dynamics. Broadband oscillations mainly include low-frequency oscillations, sub-synchronous oscillations, and super-synchronous oscillations, dominated by power electronic devices, as well as mid-high frequency oscillations [5]. Therefore, in order to reduce the harm of broadband oscillation to the power system, higher demands have been set for the detection and identification of broadband oscillation [6]. The two methods for analyzing broadband oscillation are model-driven and data-driven, the former of which includes analytical calculation [7] and numerical simulation [8]. Due to the time-varying and multi-modal nature of broadband oscillation, the causes are complex, and the model-driven method is limited by the establishment of a system model [9] and the accuracy and stability of numerical simulation [10]. At present, with the continuous development of intelligent algorithms and dynamic monitoring technology, this broadband oscillation issues analysis is based on a large volume of system measurement data collected by the wide area measurement system (WAMS) and the phasor measurement unit (PMU). An artificial intelligence algorithm is used to study the broadband oscillation problem. Therefore, in this analysis of broadband oscillation, the data-driven method can be used without the limitations of a system model and numerical simulation accuracy [11]. This oscillation information detection method, based on PMU, analyzes the data collected by the substation, compares the obtained oscillation parameters with the theoretical values, and finally transmits information about the signal frequency and amplitude to the main station [12].