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基于GRU-Dropout网络的避雷器阻性电流预测研究

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MOA(氧化锌避雷器)阻性电流在设备过电压时具有引导、分流和保护的作用,通过对其进行准确预测能够有效保证电力系统的安全与稳定.针对部分单一网络过拟合、泛化能力不足等弊端提出了一种基于GRU(门控循环单元)结合Dropout(随机失活)的MOA阻性电流预测方法.结合变电站实际电流数据,并设置合适的超参数对网络进行训练和分析.实验结果表明,采用GRU-Dropout网络相较于LSTM提高了泛化能力,减少了过拟合;相较于RNN和BP神经网络提高了精度,其最优平均绝对误差和均方根误差为1.98%和2.73%.在实际应用中能够较为准确地预测MOA对地绝缘趋势,具有一定的应用价值.
Study of Lightning Arrester Resistive Current Prediction Based on GRU-Dropout Network
MOA(metal oxide arrester)resistive current has the role of guiding,shunting and protecting during equip-ment overvoltage,and its accurate prediction can effectively ensure the safety and stability of the power system.A MOA resistive current prediction method based on GRU(gated recurrent unit)combined with Dropout(stochastic deac-tivation)is proposed to address the drawbacks of overfitting and insufficient generalization ability of some single net-works.The network is trained and analyzed by combining the actual current data of the substation and setting ap-propriate hyperparameters.The experimental results show that the use of GRU-Dropout network improves the general-ization ability and reduces overfitting compared with LSTM.Improves the accuracy compared with RNN and BP neu-ral networks,with its optimal average absolute error and root mean square error of 1.98%and 2.73%.It can predict the trend of MOA insulation to ground more accurately in practical applications,and has certain application value.

metal oxide arrester(MOA)resistive currentgated neural networkstochastic deactivatio

刘谱辉、周国平、周逸鹏

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南京林业大学 信息科学技术学院,南京 210018

南京林业大学 人工智能学院,南京 210018

氧化锌避雷器 阻性电流 门控神经网络 随机失活

国家自然科学基金资助项目

32171788

2024

自动化与仪表
天津市工业自动化仪表研究所 天津市自动化学会

自动化与仪表

CSTPCD
影响因子:0.548
ISSN:1001-9944
年,卷(期):2024.39(8)
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