Search Results (9)

The increasing proportion of wind power integration in the power systems and the dynamic interaction of power electronic equipment lead to frequent subsynchronous oscillation, which seriously threatens the safety and stability of the system. Therefore, it is urgent to develop a high-precision and robust identification method. Traditional standalone identification methods are vulnerable to wind speed fluctuations and noise, resulting in unsatisfactory accuracy and robustness. To accurately extract the oscillation parameters from the active power signal, this paper proposes a hybrid method for identifying subsynchronous oscillation in power systems. First, the active power signal is preprocessed using the wavelet threshold denoising strategy, which effectively filters out noise through multi-scale decomposition and signal reconstruction. Second, VMD is applied to the preprocessed signal to decompose it into intrinsic mode functions, thereby achieving effective separation of different oscillatory characteristics. Finally, the fast Fourier transform is used to perform spectral analysis on each IMF to accurately capture the dominant frequency and amplitude of each oscillating component. On the four-machine two-area system with DFIG, the verification is carried out under the wind speeds of 9 m/s, 10.5 m/s and 12 m/s and the no noise, 60 dB and 40 dB noise conditions. SSO is excited by inserting 20 Hz, 30.5 Hz or 40 Hz subsynchronous oscillation components into the external part of the mechanical power input of the generator. The results show that the relative error of frequency identification of the proposed method is less than 0.0426% under all test conditions. The relative error of amplitude identification is less than 1.4155%. Compared with different methods, the proposed method exhibits excellent performance under noise conditions and has robustness to wind change and noise interference. Full article

To address the problems of modal aliasing, limited identification accuracy, and inadequate noise adaptability in the parameters identification of sub-synchronous oscillation (SSO) in direct-drive permanent magnet synchronous generator (D-PMSG), a method based on improved variational mode decomposition (VMD) and total least squares matrix pencil (TLS-MP) is proposed. The grid-connected current of the D-PMSG, acquired by the phasor measurement unit (PMU), is decomposed through VMD, which is optimized via the Bayesian optimization (BO) algorithm to determine the optimal number of intrinsic mode functions (IMFs) K and the penalty factor α. By this means, mode mixing phenomena in VMD are eliminated, and noise adaptability is reinforced. The derived IMFs are subjected to mutual information (MI) analysis with the grid-connected current, from which the dominant IMFs are extracted. Each dominant IMF is subsequently resampled, and its parameters are identified through TLS-MP. In this process, the strength Pareto evolutionary algorithm II (SPEA2) is employed to improve the MP method, and the optimal signal subspace order g is obtained, which facilitates improved identification accuracy and noise adaptability. Finally, TLS is incorporated to accomplish the identification of characteristic parameters of the D-PMSG SSO components, including amplitude, frequency, phase, and damping factor. Simulation analyses based on composite signals and a four-machine two-area system model containing a direct-drive wind farm are conducted, and the effectiveness of the proposed identification method is validated. Full article

The large-scale grid integration of photovoltaic systems, accompanied by extensive power electronic equipment, exacerbates the risk of sub-synchronous oscillation (SSO) and poses a serious threat to the safe and stable operation of modern power systems. To address the limitation that traditional additional damping controllers rely on accurate mathematical models of the system, this paper applies model-free adaptive control (MFAC) to suppress sub-synchronous oscillation in photovoltaic systems. The proposed method requires no prior identification of the plant model and achieves adaptive control by online estimation of pseudo-partial derivatives using only system input-output data, with parameters optimized by particle swarm optimization. Simulation results show that the proposed controller can effectively shorten the settling time and suppress oscillations However, for oscillations induced by different mechanisms, it still has the limitation of requiring parameter re-optimization. This approach provides a new model-free technical pathway for sub-synchronous oscillation mitigation in grid-connected photovoltaic systems. Full article

This work explores the issue of identifying sub-synchronous oscillations (SSOs). Regular detection techniques face issues with response timings to variations in viewpoint and adaptability to variations in conditions of the system but our proposed method overcomes them. We have actually come up with a new framework called Tempered Fractional Deterministic Learning (TF-DL) that successfully combines tempered fractional calculus with deterministic learning theory. This method makes a memory-based learner that works best for oscillatory dynamics. This lets SSO identification happen faster through a recursive structure that can run in real time. Theoretical analysis validates exponential convergence in the context of persistent excitation. Simulations show that detection time is 62.7% shorter than gradient descent, with better convergence and better parameters. Full article

Subsynchronous oscillation (SSO) is the resonance between a new energy generator set and a weak power grid, and the resonance frequency is usually the sub-/super-synchronous frequency. The eigensystem realization algorithm (ERA) is a classic algorithm for extracting modal parameters based on matrix decomposition. By leveraging the ERA’s simplicity and low computational cost, an enhanced methodology for identifying the key parameters of SSO is introduced. The enhanced algorithm realizes SSO angular frequency extraction by constructing an angular frequency fitting equation, enabling efficient identification of SSO parameters using only a 200 ms synchrophasor sequence. In the process of identification, the fitting-based ERA effectively addresses the limitation of the existing ERA. The accuracy of SSO parameter identification is improved, thereby realizing that SSO parameter identification can be carried out using a 200 ms data window. The fitting-based ERA is verified using synthetic and actual data from synchrophasor measurement terminals. The research results show that the proposed algorithm can accurately extract fundamental and subsynchronous or supersynchronous oscillation parameters, effectively realizing dynamic real-time monitoring of subsynchronous oscillations. Full article

The subsynchronous oscillation caused by the resonance between power electronic devices and series compensation devices or weak power grids introduced by large-scale renewable energy generation greatly reduces the transmission capacity of the system and may endanger the safe operation of the power system. It even leads to system oscillation instability. In this paper, based on the advantages of a simple solution, a small amount of calculation and anti-noise of ERA, a method of subsynchronous oscillation parameter identification based on the eigensystem realization algorithm (ERA) is proposed. The Hankel matrix in the improved ERA is obtained by splicing the real part matrix and the imaginary part matrix of the synchrophasor, thus solving the problem of angular frequency conjugate constraints of two fundamental components and two oscillatory components which are not considered in the existing ERA. The solution to this problem is helpful to improve the accurate parameter identification results of ERA under the data window of 200 ms and weaken the limitation caused by the assumption that the synchrophasor model is fixed. The practicability of the improved method based on PMU is verified by the synthesis of ERA and the actual measurement data. Compared with the existing ERA, the improved ERA can accurately identify the parameters of each component under the ultra-short data window and realize the dynamic monitoring of power system subsynchronous oscillation. Full article

In rail transit traction, synchronous reluctance machines (SynRMs) are potential alternatives to traditional AC motors due to their energy-saving and low-cost characteristics. However, the nonlinearities of SynRMs are more severe than permanent magnet synchronous motors (PMSM) and induction motors (IM), which means the characteristics of SynRMs are challenging to model accurately. The parameter identification directly influences the modeling of nonlinearity, while the existing algorithms tend to converge prematurely. To overcome this problem, in this paper, a hybrid optimizer combining the SCA with the SSO algorithm is proposed to obtain the parameters of SynRMs, and the proposed Sine-Cosine self-adaptive synergistic optimization (SCSSO) algorithm preserves the self-adaptive characteristic of SSO and the exploration ability of SCA. Comprehensive numerical simulation and experimental tests have fully demonstrated that the proposed method has obviously improved parameter identification accuracy and robustness. In the dq-axis flux linkage, the mismatch between reference and estimated data of proposed algorithm is below 1% and 6%, respectively. Moreover, the best d-axis RMSE of SCSSO is 50% of the well-known algorithm CLPSO and 25% of BLPSO and its performance has improved by two orders of magnitude compared to traditional simple algorithms. In the q-axis, the best RMSE is 10% of CLPSO and 50% of Rao-3 and Jaya. Moreover, the performance of the proposed algorithm has improved nearly 90 times compared to traditional simple algorithms. Full article

The subsynchronous damping controller (SSDC) has been widely recognized for its excellent performance and low cost in subsynchronous oscillation (SSO) mitigation for the doubly fed induction generator (DFIG)-based wind power system. However, the existing SSDCs are various and lack a systematic comparison. To fill this gap, the structures and parameter design methods of common SSDCs are sorted and compared in this paper. It is found that the rotor-current-based method performs best in terms of dynamic performance and robustness, as it can mitigate SSO for all working conditions in the test, while the feasibility range of other methods is much smaller. Therefore, the influence of different parameters in a rotor-current-based SSDC on SSO mitigation is further researched, leading to a guideline for parameter selection. More importantly, to address the challenge of time-varying oscillation frequency, an adaptive frequency selection method is proposed based on the eigensystem realization algorithm, which can accurately track the SSO frequency within 5~45 Hz. The results of the root locus analysis and hardware-in-the-loop experiment demonstrate that the improved rotor-current-based SSDC performs better than other existing methods, and it does not affect the normal operation of the DFIG. Full article

The most classical subsynchronous oscillation (SSO) mode extraction methods have some shortcomings, such as lower mode identification and poor anti-noise properties. Thus, this paper proposes a new time-frequency analysis method, namely, synchrosqueezed wavelet transforms (SWT). SWT combines the advantages of empirical mode decomposition (EMD) and wavelet, which has the adaptability of EMD, and improve the ability of anti-mode mixing on EMD and wavelet. Thus, better anti-noise property and higher mode identification can be achieved. Firstly, the SSO signal is transformed by SWT and its time-frequency spectrum is obtained. Secondly, the attenuation characteristic of each intrinsic mode type (IMT) component in its time-frequency spectrum is analyzed by an automatic identification algorithm, and determine which IMT component needs reconstruction. After that, the selected IMT components with divergent characteristic are reconstructed. Thirdly, high-accuracy detection for mode parameter identification is achieved by the Hilbert transform (HT). Simulation and application examples prove the effectiveness of the proposed method. Full article