Data-Driven Optimal Power Flow: A Physics-Informed Machine Learning Approach

@article{Lei2021DataDrivenOP,
  title={Data-Driven Optimal Power Flow: A Physics-Informed Machine Learning Approach},
  author={Xingyu Lei and Zhifang Yang and Juan Yu and Junbo Zhao and Qian Gao and Hongxin Yu},
  journal={IEEE Transactions on Power Systems},
  year={2021},
  volume={36},
  pages={346-354}
}
This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework. SELM has a fast training speed and does not require the time-consuming parameter tuning process compared with the deep learning algorithms. However, the direct application of SELM for OPF is not tractable due to the complicated relationship between the system operating status and the OPF solutions. To this end, a data-driven OPF regression framework is… 

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