Abstract：This article reviews the development history of deep learning and introduces the details of the basic network of deep learning—the structure and characteristics of deep neural networks. Based on this, it analyzes the derivatives and variants that it generates. Summed up the main factors in the development of deep learning. Combining with the situation of grid intelligence, the feasibility and necessity of combining deep learning with grid intelligence are described, and the application of current deep learning in power systems is briefly introduced.
[1] 孟丹. 基于深度学习的图像分类方法研究[D]. 上海: 华东师范大学, 2017. [2] Rosenblatt F.The perceptron-a perceiving and recognizing automaton[Z]. 1957. [3] Werbos P.Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD thesis, Harvad Univ, 1974. [4] Rumelhart D E, Hinton G E, Williams R J.Learning representations by back-propagating errors. Nature, 1986, 323: 533-536. [5] LeCun Y, Bengio Y, Hinton G. Deep learning nature. 2015, 521: 436-444. [6] Hinton G E, Salakhutdinov R R.Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. [7] Hinton G E, Osindero S, Teh Y W.A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554. [8] Glorot X, Bordes A, Bengio Y.Deep sparse rectifier neural networks. In Proc. 14th International Con- ference on Artificial Intelligence and Statistics, 2011. [9] 余凯, 贾磊, 陈雨强, 等. 深度学习的昨天、今天和明天[J]. 计算机研究与发展, 2013, 50(9): 1799-1804. [10] 杨佳驹. 基于MapReduce和深度学习的负荷分析与预测[D]. 南京: 东南大学, 2016. [11] Bangalore P, Tjernberg L B.An artificial neural network approach for early fault detection of gearbox bearings[J]. IEEE Transactions on Smart Grid, 2015, 6(2): 980-987. [12] He Youbiao, Mendis G J, Wei Jin.Real-Time detection of false data injection attacks in smart grid: a deep Learning-Based intelligent mechanism[J]. IEEE Transactions on Smart Grid, 2017, 8(5): 2505-2516. [13] 高强, 阳武, 李倩. DBN层次趋势研究及其在航拍图像故障识别中的应用[J]. 仪器仪表学报, 2015(36): 1267-1274. [14] 王万国, 田兵. 基于RCNN的无人机巡检图像电力小部件识别研究. 地球信息科学学报, 2017(19): 256-263. [15] 刘辉海, 赵星宇, 赵洪山, 等. 基于深度自编码网络模型的风电机组齿轮箱故障检测[J]. 电工技术学报, 2017, 32(17): 156-163. [16] 王晓辉, 朱永利, 王艳, 等. 基于深度学习的电容器介损角在线辨识[J]. 电工技术学报, 2017, 32(15): 145-152.
2018, Vol. 19 
