Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 34, Issue 4, Pages 3303-3305Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2019.2914860
Keywords
AC power flow; deep convolutional neural network (deep CNN); data-driven; image processing; N-1 contingency screening
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Funding
- CURENT research center
- ISO New England
- NSF [ECCS-1809458]
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The increasing penetration of renewable energy makes the traditional N-1 contingency screening highly challenging when a large number of uncertain scenarios need to he combined with contingency screening. In this letter, a novel data-driven method, similar to image-processing technique, is proposed for accelerating N-1 contingency screening of power systems based on the deep convolutional neural network (CNN) method for calculating AC power flows under N-1 contingency and uncertain scenarios. Once the deep CNN is well trained, it has high generalization and works in a nearly computation-free fashion for unseen instances, such as topological changes in the N-1 cases and uncertain renewable scenarios. The proposed deep CNN is implemented on several standard IEEE test systems to verify its accuracy and computational efficiency. The proposed study constitutes a solid demonstration of the considerable potential of the data-driven deep CNN in future online applications.
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