Optimal Grid-Support Strategy With Inverter-Interfaced Distributed Generators for Short-Term Voltage Stability Improvement

Lv, Zhe; Wang, Bin; Guo, Qinglai; Sun, Hongbin

doi:10.1109/tste.2023.3301569

Cited by 2 publications

(1 citation statement)

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“…Future research could explore the stability assessment under partial PMU information missing scenarios. Additionally, employing realworld data would be valuable for further examining the performance of the proposed method [42,43].…”

Section: ⅵ Conclusionmentioning

confidence: 99%

Deep Learning for Short-Term Voltage Stability Assessment of Power Systems

Zhang

et al. 2021

IEEE Access

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To fully learn the latent temporal dependencies from post-disturbance system dynamic trajectories, deep learning is utilized for short-term voltage stability (STVS) assessment of power systems in this paper. First of all, a semi-supervised cluster algorithm is performed to obtain class labels of STVS instances due to the unavailability of reliable quantitative criteria. Secondly, a long short-term memory (LSTM) based assessment model is built through learning the time dependencies from the post-disturbance system dynamics. Finally, the trained assessment model is employed to determine the systems stability status in real time. The test results on the IEEE 39-bus system suggest that the proposed approach manages to assess the stability status of the system accurately and timely. Furthermore, the superiority of the proposed method over traditional shallow learning-based assessment methods has also been proved.

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Section: ⅵ Conclusionmentioning

confidence: 99%