Data-Driven Subsynchronous Oscillation Identification Using Field Synchrophasor Measurements

With the increasing development of renewable energy and high-voltage direct current (HVDC) transmission technology, the integration of power electronics-interfaced components has caused sub-/super-synchronous inter-harmonics into power signals, yielding the subsynchronous oscillations (SSOs). Online monitoring and identification of SSOs are crucial to the secure and stable operation of power systems. This paper proposes a data driven SSOs identification method using realistic phasor measurement unit (PMU) data. Three features are extracted via the analysis of a large number of historical PMU data, which can reflect the SSO features and the interference level. An SSOs identification method is proposed through a multiple-support vector machine (SVM) model. It allows us to adaptively select the proper classifier among the multiple-SVM models according to the interference level estimated by the extracted features. This significantly improves the identification accuracy under both low and high interference inputs of a practical power grid. An automatic update function based on incremental learning is also developed to enhance the model performance when the SSO data are subject to new features. Experimental results using field-measured PMU data validate the advantages of the proposed method over other alternatives.