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Regular Paper | Open Access

Convolution Neural Network-based Load Model Parameter Selection Considering Short-term Voltage Stability

Ying Wang1, Chao Lu2,3( ), Xinran Zhang4
School of Technology, Beijing Forestry University, Beijing 100083, China
State Key Laboratory of Control and Simulation of Power System
Generation Equipment, Tsinghua University, Beijing 100084, China
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China
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Abstract

The recently proposed ambient signal-based load modeling approach offers an important and effective idea to study the time-varying and distributed characteristics of power loads. Meanwhile, it also brings new problems. Since the load model parameters of power loads can be obtained in real-time for each load bus, the numerous identified parameters make parameter application difficult. In order to obtain the parameters suitable for off-line applications, load model parameter selection (LMPS) is first introduced in this paper. Meanwhile, the convolution neural network (CNN) is adopted to achieve the selection purpose from the perspective of short-term voltage stability. To begin with, the field phasor measurement unit (PMU) data from China Southern Power Grid are obtained for load model parameter identification, and the identification results of different substations during different times indicate the necessity of LMPS. Meanwhile, the simulation case of Guangdong Power Grid shows the process of LMPS, and the results from the CNN-based LMPS confirm its effectiveness.

Keywords

Ambient signalCNNfield PMU dataload model parameter selectionshort-term voltage stability

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CSEE Journal of Power and Energy Systems
Volume 10 Issue 3,
May 2024
Pages 1064-1074
DOI: 10.17775/CSEEJPES.2021.02580
Cite this article:
Wang Y, Lu C, Zhang X. Convolution Neural Network-based Load Model Parameter Selection Considering Short-term Voltage Stability. CSEE Journal of Power and Energy Systems, 2024, 10(3): 1064-1074. https://doi.org/10.17775/CSEEJPES.2021.02580

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Received: 04 April 2021
Revised: 10 August 2021
Accepted: 07 September 2021
Published: 05 September 2022
© 2021 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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