AI-enabled traveling wave protection for microgrids

Etingov, Dmitrii A.; Zhang, Peng; Tang, Zefan; Zhou, Yifan

doi:10.1016/j.epsr.2022.108078

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AI-enabled traveling wave protection for microgrids

Etingov, Dmitrii A.·Zhang, Peng·Tang, Zefan·Zhou, Yifan

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Abstract

Grid forming inverters provide voltage and frequency regulations for microgrids; in the meantime, new challenges are introduced for microgrid protections. For instance, inverters’ control strategies can affect protection behaviors, and low short-circuit ratios and bi-directional power flows also make protection operations complex. Protection schemes based on conventional principles such as overcurrent and distance relays do not always provide reliable, sensitive, or selective operations. Here, we devise a traveling wave protection approach for microgrids using a wavelet-driven deep neural network named WaveletKernelNet (WKN). Compared with conventional methods, the presented approach provides enhanced sensitivity, higher selectivity, and better identification of various faults in microgrids. Extensive case studies validate the efficacy and excellent performance of the devised approach.

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29 ENERGY PLANNING, POLICY, AND ECONOMY Continuous wavelet transform Convolutional neural network Learning-based protection Microgrid protection Traveling wave protection

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Etingov, Dmitrii A., Zhang, Peng, Tang, Zefan, Zhou, Yifan. 2022-05-19. AI-enabled traveling wave protection for microgrids. https://doi.org/10.1016/j.epsr.2022.108078

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