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Open Access

A Super-resolution Perception-based Incremental Learning Approach for Power System Voltage Stability Assessment with Incomplete PMU Measurements

Chao Ren, Yan Xu, Junhua Zhao, Rui Zhang ( ), Tong Wan
Interdisciplinary Graduate School, Nanyang Technological University, Singapore
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), China
Changsha University of Science and Technology, Changsha 410114, China
School of Electrical and Information Engineering, University of Sydney, Sydney, Australia
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Abstract

This paper develops a fully data-driven, missing-data tolerant method for post-fault short-term voltage stability (STVS) assessment of power systems against the incomplete PMU measurements. The super-resolution perception (SRP), based on a deep residual learning convolutional neural network, is employed to cope with the missing PMU measurements. The incremental broad learning (BL) is used to rapidly update the model to maintain and enhance the online application performance. Being different from the state-of-the-art methods, the proposed method is fully data-driven and can fill up missing data under any PMU placement information loss and network topology change scenario. Simulation results demonstrate that the proposed method has the best performance in terms of STVS assessment accuracy and missing-data tolerance among the existing methods on the benchmark testing system.

Keywords

Data-drivendeep residual convolutional neural networkincremental broad learningshort-term voltage stabilitysuper-resolution perception

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CSEE Journal of Power and Energy Systems
Volume 8 Issue 1,
January 2022
Pages 76-85
DOI: 10.17775/CSEEJPES.2020.05930
Cite this article:
Ren C, Xu Y, Zhao J, et al. A Super-resolution Perception-based Incremental Learning Approach for Power System Voltage Stability Assessment with Incomplete PMU Measurements. CSEE Journal of Power and Energy Systems, 2022, 8(1): 76-85. https://doi.org/10.17775/CSEEJPES.2020.05930

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Received: 08 November 2020
Revised: 08 February 2021
Accepted: 26 February 2021
Published: 30 April 2021
© 2020 CSEE
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