Convolution Neural Network-Based Load Model Parameter Selection Considering Short-Term Voltage Stability

Ying Wang; Chao Lu; Xinran Zhang

doi:10.17775/CSEEJPES.2021.02580

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Convolution Neural Network-Based Load Model Parameter Selection Considering Short-Term Voltage Stability

Regular Papers | 更新时间：2026-10-15

- Convolution Neural Network-Based Load Model Parameter Selection Considering Short-Term Voltage Stability
- Convolution Neural Network-Based Load Model Parameter Selection Considering Short-Term Voltage Stability
- 中国电机工程学会电力与能源系统学报(英文) 2024年10卷第3期页码：1064-1074
- 作者机构：
  
  1. School of Technology, Beijing Forestry University,Beijing,China
  2. State Key Laboratory of Control and Simulation of Power System and Generation Equipment, Tsinghua University,Beijing,China
  3. School of Automation Science and Electrical Engineering, Beihang University,Beijing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.02580
  中图分类号：
- 纸质出版：2024-05-01
- 稿件说明：
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Ying Wang, Chao Lu, Xinran Zhang. Convolution Neural Network-Based Load Model Parameter Selection Considering Short-Term Voltage Stability[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(3):1064-1074.

Ying Wang, Chao Lu, Xinran Zhang. Convolution Neural Network-Based Load Model Parameter Selection Considering Short-Term Voltage Stability[J]. CSEE Journal of Power and Energy Systems, 2024, 10(3): 1064-1074.
Ying Wang, Chao Lu, Xinran Zhang. Convolution Neural Network-Based Load Model Parameter Selection Considering Short-Term Voltage Stability[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(3):1064-1074. DOI： 10.17775/CSEEJPES.2021.02580.

Ying Wang, Chao Lu, Xinran Zhang. Convolution Neural Network-Based Load Model Parameter Selection Considering Short-Term Voltage Stability[J]. CSEE Journal of Power and Energy Systems, 2024, 10(3): 1064-1074. DOI： 10.17775/CSEEJPES.2021.02580.

摘要

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.

关键词

Keywords

references

12. MENU. ( 2017, Jul. 11 ). NASPI PMU map . [Online ] . Available: ‘ https://www.naspi.org/node/749.

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