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

• URL: http://arxiv.org/abs/2006.00544v1
• Date: Sun, 31 May 2020 15:41:24 GMT
• Title: Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach
• Authors: Xingyu Lei, Zhifang Yang, Juan Yu, Junbo Zhao, Qian Gao, Hongxin Yu
• Abstract summary: This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework. A data-driven OPF regression framework is developed that decomposes the OPF model features into three stages. Numerical results carried out on IEEE and Polish benchmark systems demonstrate that the proposed method outperforms other alternatives.
• Score: 6.5382276424254995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 developed that decomposes the OPF model features into three stages. This not only reduces the learning complexity but also helps correct the learning bias. A sample pre-classification strategy based on active constraint identification is also developed to achieve enhanced feature attractions. Numerical results carried out on IEEE and Polish benchmark systems demonstrate that the proposed method outperforms other alternatives. It is also shown that the proposed method can be easily extended to address different test systems by adjusting only a few hyperparameters.

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