## 1 Introduction  
In the modern energy system involving renewable energy, with the large-scale grid integration of new energy power generation and the widespread application of various high-capacity power electronic devices, the problem of subsynchronous oscillations (SSOs) has become increasingly serious [1, 2, 3, 4, 5, 6]. SSOs occur when the voltage and current frequency in a power supply system are less than the synchronous frequency. In certain situations, the presence of higher-frequency supersynchronous components may also be observed. In light of the multifaceted negative impacts of SSOs on power systems, such as reducing power transmission efficiency, endangering overall system stability, and triggering instability, we must attach great importance to and strengthen comprehensive monitoring and in-depth analysis of their dynamic processes within a system. Swift and effective control measures must be implemented to prevent SSOs from occurring, ensuring the stability and reliability of a power grid [7, 8]. There are many methods for identifying SSO parameters. However, the existing algorithm mainly has two shortcomings; the first is the poor accuracy of SSO parameter recognition, and the second one is the long data window. Several methods exist for detecting the dynamic processes associated with subsynchronous oscillations using fault recorders (FRs) [9, 10, 11]. However, acquiring instantaneous data promptly poses a challenge, as these data are primarily sourced from FRs located at various buses. Moreover, the need for unified timing standards for FRs complicates the process of dynamically and comprehensively observing subsynchronous oscillations. Currently, the wide area measurement system (WAMS) and phasor measurement units (PMUs) have achieved widespread adoption in modern power grids [12; 13; 14]. The biggest highlight of using synchrophasor data for SSO analysis lies in its ability to integrate measurement data from different power stations onto a unified time base, facilitating direct and accurate comparisons. Among the prevalent SSO parameter identification techniques incorporating synchrophasors, algorithms rooted in the discrete Fourier transform (DFT) have garnered significant attention and research. In [15], the specific impacts of the discrete Fourier transform algorithm and the sampling rate of synchrophasors on the magnitude spectrum were analyzed utilizing a WAMS and PMUs. This furnished a correction ratio which enabled precise identification of SSO parameters, thereby enhancing the accuracy of the analysis. However, the requirement of ensuring at least a 10 s data window for accurate extraction of SSO parameters when adopting this method may impose certain limitations on its reliability and real-time response capability in capturing rapidly fluctuating SSOs. Hence, several researchers have introduced an interpolation-DFT method improved by employing a Hann-window [16; 17]. This innovative approach both shrinks the necessary data window down to 2 s only and successfully mitigates the impact of spectrum aliasing, enhancing the overall accuracy and efficiency of the analysis. Among the non-DFT algorithms, two primary modal parameter extraction methods exist which rely on the synchrophasor sequence [18]. These two methods are the eigensystem realization algorithm (ERA) [19] and the matrix pencil method (MPM) [20; 21; 22]. The spectral resolution poses fewer constraints on non-DFT algorithms, allowing for the utilization of a shorter data window of 2 s. While the method mentioned above effectively guarantees a certain level of accuracy in identifying SSO parameters, a 2 s data window remains relatively lengthy. Due to the introduction of new energy-generating units, the dynamic speed of subsynchronous oscillation is extremely fast, and thus we need to monitor subsynchronous oscillation quickly and synchronously. The existing 2 s data window is no longer sufficient for fast and synchronous monitoring of SSO.