@Article{ee.2023.042633,
AUTHOR = {Xueting Cheng, Ziqi Zhang, Yueshuang Bao, Huiping Zheng},
TITLE = {Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms},
JOURNAL = {Energy Engineering},
VOLUME = {120},
YEAR = {2023},
NUMBER = {11},
PAGES = {2517--2529},
URL = {http://www.techscience.com/energy/v120n11/54420},
ISSN = {1546-0118},
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.},
DOI = {10.32604/ee.2023.042633}
}