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Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms

Xueting Cheng1, Ziqi Zhang2,*, Yueshuang Bao1, Huiping Zheng1

1 State Grid Shanxi Electric Power Research Institute, State Grid Shanxi Electric Power Co., Ltd., Taiyuan, 030001, China
2 School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, 102206, China

* Corresponding Author: Ziqi Zhang. Email: email

(This article belongs to the Special Issue: Fault Diagnosis and State Evaluation of New Power Grid)

Energy Engineering 2023, 120(11), 2517-2529. https://doi.org/10.32604/ee.2023.042633

Abstract

At present, the proportion of new energy in the power grid is increasing, and the random fluctuations in power output increase the risk of cascading failures in the power grid. In this paper, we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering (DEC) algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids. First, considering the real-time operation status and system structure of new energy power grids, the scenario cascading failure risk indicator is established. Based on this indicator, the risk of cascading failure is calculated for the scenario set, the scenarios are clustered based on the DEC algorithm, and the scenarios with the highest indicators are selected as the significant risk scenario set. The results of simulations with an example power grid show that our method can effectively identify scenarios with a high risk of cascading failures from a large number of scenarios.

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APA Style
Cheng, X., Zhang, Z., Bao, Y., Zheng, H. (2023). Identification of high-risk scenarios for cascading failures in new energy power grids based on deep embedding clustering algorithms. Energy Engineering, 120(11), 2517-2529. https://doi.org/10.32604/ee.2023.042633
Vancouver Style
Cheng X, Zhang Z, Bao Y, Zheng H. Identification of high-risk scenarios for cascading failures in new energy power grids based on deep embedding clustering algorithms. Energ Eng. 2023;120(11):2517-2529 https://doi.org/10.32604/ee.2023.042633
IEEE Style
X. Cheng, Z. Zhang, Y. Bao, and H. Zheng "Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms," Energ. Eng., vol. 120, no. 11, pp. 2517-2529. 2023. https://doi.org/10.32604/ee.2023.042633



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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