基于图像特征筛选与融合网络的电力系统动态稳定评估

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Abstract During the feature extraction process of traditional neural networks, some shallow information may be lost, which leads to a decrease in the accuracy of model evaluation. In order to fully utilize the feature information of each layer, in this paper, the feature fusion module is embedded in traditional neural networks. An image feature filtering and fusion network is proposed. The network first fuses the features of each layer through linear and nonlinear changes to improve the expression of effective features. Then, the feature fusion weights are quantified based on Pearson correlation. Finally, weighted fusion is performed on the features of each layer based on the feature fusion weights. The simulation experiments are carried out in IEEE 39-bus system and IEEE 145-bus system. The results show that the proposed network has better evaluation performance compared to traditional neural networks.

Key words： neural network feature fusion feature filtering dynamic stability assessment image recognition

Received: 11 October 2023

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	LU Yufei
	LIN Jianxin

Cite this article:

LU Yufei,LIN Jianxin. Dynamic stability assessment of power system based on image feature filtering and fusion network[J]. Electrical Engineering, 2023, 24(12): 1-6.

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http://dqjs.cesmedia.cn/EN/Y2023/V24/I12/1

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