Application of adaptive chirp mode decomposition on parameter identification of sub/super‐synchronous oscillation signals

Abstract Identifying the parameters of sub/super‐synchronous oscillation (SubSO/SupSO) signals is challenging, because (1) the mode numbers of the oscillation signals are unknown, and (2) the oscillation signals are non‐stationary and noisy. The non‐stationary components need to be accurately identified in order to efficiently suppress SubSO/SupSO. Here, an adaptive chirp mode decomposition (ACMD) method joining a synchroextracting transform (SET) is exploited. First, for detecting the initial instantaneous frequencies (IFs) of SubSO and SupSO, SET is employed to provide time–frequency representations with satisfied energy concentration to ACMD. Second, ACMD is applied to decomposing the main components from the oscillation signal without needing to use the mode numbers, and directly compute the final IFs and instantaneous amplitudes (IAs) for SubSO and SupSO without Hilbert transform. And third, the damping factors of the SubSO and SupSO are further identified with the IAs. The proposed method is evaluated over synthetic signals, simulation data, and real field data from a wind system. Experimental results demonstrate that, compared with the classical signal decomposition‐based identification methods, the proposed method more accurately detects frequencies, amplitudes, and damping factors of SubSO and SupSO components for non‐stationary and noisy signals.

Abstract licence: CC BY-ND

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