Meng Zhan

Leyou Zhou, Mucheng Li, Xiaojie Shi et al.

Broadband oscillations in wind farms have been widely reported in recent years. Past studies have examined various types of oscillations in wind farms, relating small-signal stability to control settings, operating conditions, and electrical parameters. However, most analyses are performed on aggregated single-unit models, which may deviate from the true behavior, leading to misleading stability assessments. To investigate how aggregation affects stability conclusions, this paper develops detailed single-, two-, and three-unit doubly-fed induction generator (DFIG) models and their aggregated counterparts. Then, a D-decomposition-related ray-extrapolation method is proposed to characterize the small-signal stability region of nonlinear DFIG models in the parameter space, delineating stability boundaries under numerous parameter combinations. The study reveals that aggregated models stability regions within the parameter planes of control settings and operating conditions differ from those of granular models in terms of basic shape, critical modes, and evolution patterns, posing a risk of misjudging stability margins.

Yao Du, Qing Li, Huawei Fan et al.

Power systems dominated by renewable energy encounter frequently large, random disturbances, and a critical challenge faced in power-system management is how to anticipate accurately whether the perturbed systems will return to the functional state after the transient or collapse. Whereas model-based studies show that the key to addressing the challenge lies in the attracting basins of the functional and dysfunctional states in the phase space, the finding of the attracting basins for realistic power systems remains a challenge, as accurate models describing the system dynamics are generally unavailable. Here we propose a new machine learning technique, namely balanced reservoir computing, to infer the attracting basins of a typical power system based on measured data. Specifically, trained by the time series of a handful of perturbation events, we demonstrate that the trained machine can predict accurately whether the system will return to the functional state in response to a large, random perturbation, thereby reconstructing the attracting basin of the functional state. The working mechanism of the new machine is analyzed, and it is revealed that the success of the new machine is attributed to the good balance between the echo and fading properties of the reservoir network; the effect of noisy signals on the prediction performance is also investigated, and a stochastic-resonance-like phenomenon is observed. Finally, we demonstrate that the new technique can be also utilized to infer the attracting basins of coexisting attractors in typical chaotic systems.