DBN深度学习算法在反窃电系统中的应用
|
摘要
随着经济的发展,电力需求逐渐增大,但由于电力系统在电量自动化的技术方面相对落后,窃电现象屡禁不止。传统的反窃电手段一般都围绕加强电能计量装置进行技术改造,管理效率较低,而深度学习的目的是利用构建多层神经网络模型的方法来学习图像、文本、语音等数据的潜在特征,在分类问题上有很好的效果,在众多复杂领域成功应用的深度学习算法为解决反窃电问题提供了新的有效途径。主要介绍了DBN的结构与学习算法和基于DBN算法的反窃电模型,最后进行了实验,对结果进行了分析。
With the development of economy, the electric power demand increases gradually, but because of the relative backwardness in the automation of electricity, the phenomenon of electric stealing is common. But the traditional anti electric stealing means generally centered around how to strengthen the technical transformation of the electric energy metering device, and the management efficiency is low. The purpose of deep learning is to use the method of constructing the multi-layer neural network model. To learn the potential features of image, text, voice and other data, it also has good effect on the classification problem. The successful application of the deep learning algorithm in many complex fields provides a new effective way to solve the problem of anti stealing electricity. The structure and learning algorithm of DBN and the anti-stealing model based on DBN algorithm was mainly introduced. Finally, experiments were carried out and the results were analyzed.
With the development of economy, the electric power demand increases gradually, but because of the relative backwardness in the automation of electricity, the phenomenon of electric stealing is common. But the traditional anti electric stealing means generally centered around how to strengthen the technical transformation of the electric energy metering device, and the management efficiency is low. The purpose of deep learning is to use the method of constructing the multi-layer neural network model. To learn the potential features of image, text, voice and other data, it also has good effect on the classification problem. The successful application of the deep learning algorithm in many complex fields provides a new effective way to solve the problem of anti stealing electricity. The structure and learning algorithm of DBN and the anti-stealing model based on DBN algorithm was mainly introduced. Finally, experiments were carried out and the results were analyzed.
引文
[1]张燕.非线性系统的神经网络预测控制研究[D].天津:南开大学,2004.ZHANG Y.Research on neural network predictive control for nonlinear systems[D].Tianjin:Nankai University,2004.
[2]王巍娟.基于隐层改进的BP网络在织物染色配色中的应用研究[D].青岛:青岛大学,2009.WANG W J.Application research of improved BP network based on hidden layer in fabric dyeing and color matching[D].Qingdao:Qingdao University,2009.
[3]WANG Z D,WANG B H,YAN T,et al.A distributed optical fiber monitoring system for anti-stealing of oil pipelines[J].Journal of Daqing Petroleum Institute,2008(4):20
[4]杨新佳.基于神经网络的教学质量评价模型研究[D].西安:西安科技大学,2011.YANG X J.Research on teaching quality evaluation model based on neural network[D].Xi’an:Xi’an University of Science and Technology,2011.
[5]黄丹,黄伯元.人工神经网络及其在医疗卫生中的应用[J].卫生职业教育,2005,24(4):118-119.HUANG D,HUANG B Y.Artificial neural network and its application in medical and health care[J].Health Vocational Education,2005,24(4):118-119.
[6]王映乔.基于神经网络的汇率预测及系统设计[D].成都:西南财经大学,2007.WANG Y Q.Exchange rate prediction and system design based on neural network[D].Chengdu:Southwestern University of Finance and Economics,2007.
[7]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京:清华大学出版社,2005.ZHOU K L,KANG Y H.Neural network model and MATLABsimulation program design[M].Beijing:Tsinghua University Press,2005.
[8]SAHU S,SHANDILYA S K.A comprehensive survey on intrusion detection in MANET[J].International Journal of Information Technology and Knowledge Management,2010,2(2):305-310.
[9]卢伟伟.Web文本信息过滤方法研究[D].武汉:华中科技大学,2007.LU W W.Web text information filtering method research[D].Wuhan:Huazhong University of Science and Technology,2007.
[10]潘晓.基于神经网络的相机标定[D].西安:西安科技大学,2010.PAN X.Camera calibration based on neural network[D].Xi’an:Xi’an University of Science and Technology,2010.
[11]张怡.基于深度学习的电力系统扰动后频率预测[D].济南:山东大学,2018.ZHANG Y.Deep learning based frequency prediction after power system disturbance[D].Jinan:Shandong University,2018.
[2]王巍娟.基于隐层改进的BP网络在织物染色配色中的应用研究[D].青岛:青岛大学,2009.WANG W J.Application research of improved BP network based on hidden layer in fabric dyeing and color matching[D].Qingdao:Qingdao University,2009.
[3]WANG Z D,WANG B H,YAN T,et al.A distributed optical fiber monitoring system for anti-stealing of oil pipelines[J].Journal of Daqing Petroleum Institute,2008(4):20
[4]杨新佳.基于神经网络的教学质量评价模型研究[D].西安:西安科技大学,2011.YANG X J.Research on teaching quality evaluation model based on neural network[D].Xi’an:Xi’an University of Science and Technology,2011.
[5]黄丹,黄伯元.人工神经网络及其在医疗卫生中的应用[J].卫生职业教育,2005,24(4):118-119.HUANG D,HUANG B Y.Artificial neural network and its application in medical and health care[J].Health Vocational Education,2005,24(4):118-119.
[6]王映乔.基于神经网络的汇率预测及系统设计[D].成都:西南财经大学,2007.WANG Y Q.Exchange rate prediction and system design based on neural network[D].Chengdu:Southwestern University of Finance and Economics,2007.
[7]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京:清华大学出版社,2005.ZHOU K L,KANG Y H.Neural network model and MATLABsimulation program design[M].Beijing:Tsinghua University Press,2005.
[8]SAHU S,SHANDILYA S K.A comprehensive survey on intrusion detection in MANET[J].International Journal of Information Technology and Knowledge Management,2010,2(2):305-310.
[9]卢伟伟.Web文本信息过滤方法研究[D].武汉:华中科技大学,2007.LU W W.Web text information filtering method research[D].Wuhan:Huazhong University of Science and Technology,2007.
[10]潘晓.基于神经网络的相机标定[D].西安:西安科技大学,2010.PAN X.Camera calibration based on neural network[D].Xi’an:Xi’an University of Science and Technology,2010.
[11]张怡.基于深度学习的电力系统扰动后频率预测[D].济南:山东大学,2018.ZHANG Y.Deep learning based frequency prediction after power system disturbance[D].Jinan:Shandong University,2018.