Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 34, Issue 6, Pages 5044-5052Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2019.2922671
Keywords
Data-driven; dynamic security assessment; generative adversarial networks; hybrid ensemble learning
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Funding
- Ministry of Education, Republic of Singapore [AcRF TIER 1 2017-T1-001-228 (RG92/17)]
- National Research Foundation of Singapore [NRF2018-SR2001-018]
- Nanyang Assistant Professorship from the Nanyang Technological University, Singapore
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This paper proposes a fully data-driven approach for PMU-based pre-fault dynamic security assessment (DSA) with incomplete data measurements. The generative adversarial network (GAN), which is an emerging unsupervised deep learning technique based on two contesting deep neural networks, is used to address the missing data. While the state-of-the-art methods for missing data are dependent on PMU observability, they are limited by the placement of PMU and network topologies. Distinguished from existing methods, the proposed approach is fully data-driven and can fill up incomplete PMU data independent on PMU observability and network topologies. Therefore, it is more generalized and extensible. Simulation results show that, under any PMU missing conditions, the proposed method can maintain a competitively high DSA accuracy with a much less computation complexity.
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