Inferring attracting basins of power system with machine learning

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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 new objective function adopted in optimizing the machine hyperparameters, which keeps a 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 it is found that the machine performance is improved by introducing a certain amount of noise. Finally, we demonstrate that the new technique can also be utilized to infer the attracting basins of coexisting attractors in typical chaotic systems. Published by the American Physical Society 2024