基于生成对抗网络的小样本数据生成技术研究
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摘要
基于数据驱动的深度学习技术成为新一代智能电网的应用趋势,该技术对电网中有标注训练数据的量级提出更高的要求。为了获取更多有标注的智能电网样本数据,文章提出了一种基于改进的生成对抗网络(generative adversarial network,GAN)的训练样本生成算法。该方法通过交替训练改进GAN的生成模型与判别模型,无需先验知识的指导,自主学习原始样本的分布规律,生成新的数据样本。然后采用人工神经网络作为基础分类器,计算样本分类的准确率,检验生成样本的有效性。实验表明,改进GAN模型可以有效学习样本的分布规律,提升谐波分类的准确率,该方法同时具有良好的抗噪性和泛化性,对深度学习技术在智能电网中的深入发展具有重要意义。
Data-driven deep learning technology has become the application trend of the new generation of smart grids, which puts higher demands on the magnitude of the labeled training data in the grid. In order to obtain more labeled smart grid sample data, this paper proposes a training sample generation algorithm based on improved generative adversarial network. The method improves the generation model and discriminant model of GAN through alternate training, and does not need the guidance of prior knowledge to autonomously learn the distribution law of the original samples and generate new data samples. Then the artificial neural network is used as the basic classifier to calculate the accuracy of the sample classification and verify the validity of the generated samples. Experiments have shown that the improved GAN model can effectively learn the distribution law of samples and improve the accuracy of harmonic classification. This method has good anti-noise ability and generalization, which is of great significance for development of deep learning technology in smart grid.
Data-driven deep learning technology has become the application trend of the new generation of smart grids, which puts higher demands on the magnitude of the labeled training data in the grid. In order to obtain more labeled smart grid sample data, this paper proposes a training sample generation algorithm based on improved generative adversarial network. The method improves the generation model and discriminant model of GAN through alternate training, and does not need the guidance of prior knowledge to autonomously learn the distribution law of the original samples and generate new data samples. Then the artificial neural network is used as the basic classifier to calculate the accuracy of the sample classification and verify the validity of the generated samples. Experiments have shown that the improved GAN model can effectively learn the distribution law of samples and improve the accuracy of harmonic classification. This method has good anti-noise ability and generalization, which is of great significance for development of deep learning technology in smart grid.
引文
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[19] 王刚,武毅,王梓,等.基于大数据技术的电力系统谐波分析及治理方案[J].电力系统及其自动化学报,2016,28(s1):46-50.WANG Gang,WU Yi,WANG Zi,et al.Analysis and control scheme for power system harmonics based on big data technology [J].Proceedings of the CSU-EPSA,2016,28(s1):46-50.
[2] 王成山,李鹏,于浩.智能配电网的新形态及其灵活性特征分析与应用[J].电力系统自动化,2018,42(10):13-21.WANG Chengshan,LI Peng,YU Hao.Development and characteristic analysis of flexibility in smart distribution network [J].Automation of Electric Power Systems,2018,42(10):13-21.
[3] 刘威,张东霞,王新迎,等.基于深度强化学习的电网紧急控制策略研究[J].中国电机工程学报,2018,38(1):109-119.LIU Wei,ZHANG Dongxia,WANG Xinying,et al.A decision making strategy for generating unit tripping under emergency circumstances based on deep reinforcement learning [J].Proceedings of the CSEE,2018,38(1):109-119.
[4] 郭丽丽,丁世飞.深度学习研究进展[J].计算机科学,2015,42(5):28-33.GUO Lili,DING Shifei.Research progress on deep learning [J].Computer Science,2015,42(5):28-33.
[5] 刘全,翟建伟,章宗长,等.深度强化学习综述[J].计算机学报,2018,41(1):1-27.LIU Quan,ZHAI Jianwei,ZHANG Zongchang,et al.A survey on deep reinforcement learning[J].Chinese Journal of Computers,2018,41(1):1-27.
[6] 陈伟宏,安吉尧,李仁发,等.深度学习认知计算综述[J].自动化学报,2017,43(11):1886-1897.CHEN Weihong,AN Jiyao,LI Renfa,et al.Review on deep-learning-based cognitive computing [J].Acta Automatica Sinica,2017,43(11):1886-1897.
[7] 赵俊华,董朝阳,文福拴,等.面向能源系统的数据科学:理论、技术与展望[J].电力系统自动化,2017,41(4):1-11,19.ZHAO Junhua,DONG Zhaoyang,WEN Fushuan,et al.Data science for energy systems:Theory,techniques and prospect [J].Automation of Electric Power Systems,2017,41(4):1-11,19.
[8] 张根周.大数据在智能电网领域的应用[J].电网与清洁能源,2016,32(6):114-117.ZHANG Genzhou.Applications of big data in the field of smart grid [J].Power System and Clean Energy,2016,32(6):114-117.
[9] 刘世成,张东霞,朱朝阳,等.能源互联网中大数据技术思考[J].电力系统自动化,2016,40(8):14-21,56.LIU Shicheng,ZHANG Dongxia,ZHU Chaoyang,et al.A view on big data in energy internet [J].Automation of Electric Power Systems,2016,40(8):14-21,56.
[10] 尚宇炜,马钊,彭晨阳,等.内嵌专业知识和经验的机器学习方法探索(一):引导学习的提出与理论基础[J].中国电机工程学报,2017,37(19):5560-5571,5833.SHANG Yuwei,MA Zhao,PENG Chenyang,et al.Study of a novel machine learning method embedding expertise part I:Proposals and fundamentals of guiding learning [J].Proceedings of the CSEE,2017,37(19):5560-5571,5833.
[11] 何旭,姜宪国,张沛超,等.基于SVM的小样本条件下继电保护可靠性参数估计[J].电网技术,2015,39(5):1432-1437.HE Xu,JIANG Xianguo,ZHANG Peichao,et al.SVM based parameter estimation of relay protection reliability with small samples [J].Power System Technology,2015,39 (5):1432- 1437.
[12] 段萌,王功鹏,牛常勇.基于卷积神经网络的小样本图像识别方法[J].计算机工程与设计,2018,39(1):224-229.DUAN Meng,WANG Gongpeng,NIU Changyong.Method of small sample size image recognition based on convolution neural network [J].Computer Engineering and Design,2018,39(1):224-229.
[13] KAN Yingnan,YANG Zhaojun,LI Guofa.Bayesian reliability modeling and assessment solution for CN machine tools under small-sample data[J].Chinese Journal of Mechanical Engineering,2015,28(6):1229-1239.
[14] GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial nets[C]// Proceedings of the 27th International Conference on Neural Information Processing Systems.Montreal,Canada:MIT Press,2014:2672-2680.
[15] 王坤峰,苟超,段艳杰,等.生成式对抗网络GAN的研究进展与展望[J].自动化学报,2017,43(3):321-332.WANG Kunfeng,GOU Chao,DUAN Yanjie,et al.Generative adversarial networks:The state of the art and beyond [J].Acta Automatica Sinica,2017,43(3):321-332.
[16] RADFORD A,METZ L,CHINTALA S.Unsupervised representation learning with deep convolutional generative adversarial networks[J].Computer Science,2015.
[17] QIAN L,CARTES D A,LI H.An improved adaptive detection method for power quality improvement[J].IEEE Transactions on Industry Applications,2006,44(2):525-533.
[18] 郝淑娟,何巍巍,刘永皓,等.基于神经网络的二次谐波检测研究[J].现代电子技术,2018,41(18):170-173.HAO Shujuan,HE Weiwei,LIU Yonghao,et al.Research on second harmonic detection based on neural network [J].Modern Electronics Technique,2018,41(18):170-173.
[19] 王刚,武毅,王梓,等.基于大数据技术的电力系统谐波分析及治理方案[J].电力系统及其自动化学报,2016,28(s1):46-50.WANG Gang,WU Yi,WANG Zi,et al.Analysis and control scheme for power system harmonics based on big data technology [J].Proceedings of the CSU-EPSA,2016,28(s1):46-50.
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