Abstract
In recent years, subsynchronous control interaction (SSCI) frequently occurred due to the rapid development of inverter based resources. Online monitoring and identifying SSCI are thus of great importance for the safe operation of power systems. This paper proposes a data-driven method for SSCI identification using synchrophasors generated by phasor measurement units (PMUs). The challenges of this work mainly result from two factors: 1) SSCI may simultaneously involve a sub-synchronous oscillation (Sub-SO) mode and a super-synchronous oscillation (Sup-SO) mode. The spectra of these two modes would be aliased due to the limited reporting rate of PMUs. 2) The reporting filter in PMUs significantly weakens the oscillation signal as the frequency of SSCI is generally far away from the nominal frequency. To tackle these challenges, the influence of Sub-SO and Sup-SO on spectra of synchrophasors is analyzed first. It is revealed that the positive and negative spectra are linearly correlated with Sub-SO and Sup-SO modes. Several features are then extracted via the analysis of the spectra which can reflect the SSCI features and a data-driven identification method is further proposed through a fast reduced kernel extreme learning machine (RKELM) model. Compared with the traditional classification techniques, the RKELM algorithm based on kernel function has stronger generalization performance and higher accuracy while ensuring shorter training time. This significantly improves the accuracy and efficiency of SSCI identification, especially under noise conditions. Both simulations and field tests demonstrate the effectiveness and usefulness of the proposed method.
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