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Advanced Identification Methods for Power System Oscillations based on Measurements

With the large-scale new energy sources such as photovoltaic and wind power connected to the power system via inverter-based technology, the original damping and inertia of the power system are reduced, leading to stability issues, for instance, low-frequency oscillations. Conventional numerical simulation methods are difficult to cope with the stochastic and complex control methods of huge power systems and new energy devices. The traditional modeling methods can hardly deal with the black-box system and obtain the reduced-order model of the complex power system. Whereas the measurement-based methods can identify the transfer function of the high-order system and overcome the problem. The effectiveness and accuracy of the identification model to reflect the original system dynamics are demonstrated by applying Prony analysis, matrix pencil (MP), and eigenvalue realization algorithm (ERA) methods to identify the oscillation measurements on Kundur's four-machine two-area system, type-4 wind turbine, and real oscillation events, respectively. These measurement-based methods have advantages in estimating high-order systems and are therefore more practical for identifying current inverter-based power systems than existing methods.