Transient security situational awareness of smart grids based on an improved deep belief network

被引：0

作者：

Li H. ^{[1
]}

Shen Y. ^{[1
]}

Luo Y. ^{[2
]}

机构：

[1] Department of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai

[2] Sichuan Water Conservancy Vocational College, Chengdu

来源：

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2022年 / 50卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Deep belief network; Genetic algorithm; Sample imbalance; SMOTE; Transient security situational awareness;

D O I：

10.19783/j.cnki.pspc.210654

中图分类号：

学科分类号：

摘要：

Deep learning shows superiority in transient security situational awareness of smart grids. However, it is hard to find optimal parameters for low-dimensional features and network structures in multi-layer network reconstruction. Thus an improved deep belief network (DBN) is proposed. In this method, the SMOTE algorithm is first adopted for oversampling to balance the proportion of data samples. This ensures mining of the deep structure of DBN. Then the binary neuron from Gibbs sampling is used in DBN to improve the noise immunity in the reconstruction process. Given the repeated manual debugging of network structure, a genetic algorithm (GA)-based GA-DBN model is finally employed to achieve global optimization. The low-dimensional features are abstracted accurately from the high-dimensional measurement data at the bottom layer and the classification precision is guaranteed. A test on the New England 39-bus system with imbalance and noise samples shows that the proposed method outperforms the existing methods in accuracy, F1 score and missing alarm. © 2022 Power System Protection and Control Press.

引用

页码：171 / 177

页数：6

共 24 条

[21]

WANG B, FANG B W, WANG Y J, Et al., Power system transient stability assessment based on big data and the core vector machine, IEEE Transactions on Smart Grid, 7, 5, pp. 2561-2570, (2016)

[22]

ZHOU Yanzhen, WU Junyong, YU Zhihong, Et al., Support vector machine ensemble classifier and its confidence evaluation for transient stability prediction of power systems, Power System Technology, 41, 4, pp. 1188-1196, (2017)

[23]

WU Junyong, ZHANG Ruoyu, JI Jiashen, Et al., Two-stage transient stability prediction method of power system considering cost of misdetection and false alarm, Automation of Electric Power Systems, 44, 24, pp. 44-52, (2020)

[24]

WANG Zhongzhen, HUANG Bo, FANG Zhijun, Et al., Improved SMOTE unbalanced data integration classification algorithm, Journal of Computer Applications, 39, 9, pp. 2591-2596, (2019)

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