Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《Power System Technology》 2017-08
Add to Favorite Get Latest Update

Concentration Prediction of Dissolved Gases in Transformer Oil Based on Deep Belief Networks

DAI Jiejie;SONG Hui;YANG Yi;CHEN Yufeng;SHENG Gehao;JIANG Xiuchen;Department of Electrical Engineering, Shanghai Jiaotong University;Electric Power Research Institute of Shandong Power Supply Company of State Grid;  
Prediction of development trend of gas concentration dissolved in transformer oil can provide important basis for transformer condition assessment. A new prediction model based on deep belief networks is proposed. Seven types of characteristic gas concentration combined with environment temperature and transformer oil temperature are fed to input layer. The model can automatically extract regulation of gas concentration development trend through training a multi-hidden-layer machine learning model based on restricted Boltzmann machine. Correlation between different types of gases and influence of temperatures is activated layer by layer. Irrelevant and redundant information is inhibited by the model. The proposed method has higher prediction accuracy. It overcomes drawbacks of low stability in traditional methods and shortcoming of considering only one characteristic gas. In addition, it avoids manual intervention in calculation process. Finally, case analysis verifies effectiveness and superiority of the proposed model.
【Fund】: 国家自然科学基金项目(51477100);; 国家863高技术基金项目(2015AA050204);; 国家电网公司科技项目(520626150032)~~
【CateGory Index】: TM41
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
【Citations】
Chinese Journal Full-text Database 10 Hits
1 ZHANG Xinbo;TANG Ju;PAN Cheng;ZHANG Xiaoxing;JIN Miao;YANG Dong;ZHENG Jian;WANG Ting;School of Electrical Engineering,Wuhan University;Shandong Electric Power Research Institute,State Grid Shandong Electric Power Company;;Research of Partial Discharge Recognition Based on Deep Belief Nets[J];电网技术;2016-10
2 ZHOU Xiaoli;ZHANG Feng;DU Zhenhong;CAO Minjie;LIU Renyi;Zhejiang Provincial Key Laboratory of Resources and Environmental Information System,Zhejiang University;Department of Earth Sciences,Zhejiang University;;A study on time series prediction model based on CRBM algorithm[J];浙江大学学报(理学版);2016-04
3 LIN Xiangning;HUANG Jing;XIONG Weihong;WENG Hanli;ZHU Liming;ZHANG Zhen;XIE Zhicheng;College of Electrical & New Energy ,China Three Gorges University;School of Electrical and Electronic Enginneering ,Huazhong University of Science and Technology;Central China Grid Company Limited of State Grid Corporation of China;Huaneng Beijing Thermal Power Co.,Ltd.;;Interval prediction of dissolved-gas concentration in transformer oil[J];电力自动化设备;2016-04
4 Feng Yong;Xiong Qingyu;Shi Weiren;Cao Junhua;School of Automation,Chongqing University;Chongqing Public Security Bureau;;Speaker feature extraction algorithm based on restricted Boltzmann[J];仪器仪表学报;2016-02
5 SHI Xin;ZHU Yongli;SA Churila;WANG Liuwang;SUN Gang;School of Control and Computer Engineering, North China Electric Power University;State Grid Corporation of China;;Power transformer fault classifying model based on deep belief network[J];电力系统保护与控制;2016-01
6 XU Zhengyu;WANG Ke;SUN Jiantao;ZHAO Xiaoyu;LI Yuan;China Electric Power Research Institute;School of Electrical Engineering, Xi'an Jiaotong University;;Research on Characteristics During Latent Period of Partial Discharge Developing Process Under Direct Voltage of Oil-paper Insulation[J];电网技术;2016-02
7 WU Guangning;YAO Mengxi;XINDongli;GAO Bo;School of Electrical Engineering, Southwest Jiaotong University;;Experimental Study on Oil-Impregnated Paper With Non-Uniform Thermal Aging[J];电网技术;2015-11
8 LIU Kai;WANG Peng;WANG Wei;Beijing Key Laboratory of High Voltage & EMC (North China Electric Power University);State Key Lab of Control and Simulation of Power Systems and Generation Equipments(Dept. of Electrical Engineering, Tsinghua University);;Electric Filed Distribution in Transformer Oil Under DC Electric Field[J];电网技术;2015-06
9 WEI Zhen;QI Bo;ZUO Jian;LI Chengrong;Beijing Key Laboratory of High Voltage & EMC (North China Electric Power University);State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources(North China Electric Power University);;A Method to Diagnose Defects in Oil-Paper Insulation of Converter Transformer Based on Image Feature of Partial Discharge[J];电网技术;2015-04
10 Yu Bin;Li Shaozi;Xu Suxia;Ji Rongrong;School of Information Science and Technology, Xiamen University;Fujian Key Laboratory of the Brain-like Intelligent Systems (Xiamen University);Institute of Mathematics and Computer Science, GuiZhou Normal University;;Deep Learning: A key of Stepping into the Era of Big Data[J];工程研究-跨学科视野中的工程;2014-03
【Co-citations】
Chinese Journal Full-text Database 10 Hits
1 TIAN Yan;LIU Yushun;XIONG Jun;LI Pengfei;ZHOU Wenjun;ZHONG Shaoquan;Guangzhou Power Supply Bureau Co.,Ltd.;School of Electrical Engineering,Wuhan University;;Feature Parameters Extraction Method of Partial Discharge UHF Signal Based on Textural Features in Time-frequency Representation Image[J];高压电器;2017-07
2 DAI Jiejie;SONG Hui;YANG Yi;CHEN Yufeng;SHENG Gehao;JIANG Xiuchen;Department of Electrical Engineering, Shanghai Jiaotong University;Electric Power Research Institute of Shandong Power Supply Company of State Grid;;Concentration Prediction of Dissolved Gases in Transformer Oil Based on Deep Belief Networks[J];电网技术;2017-08
3 REN Hao;QU Jian-feng;CHAI Yi;TANG Qiu;YE Xin;School of Automation,Chongqing University;State Key Laboratory of Power Transmission Equipment and System Security and New Technology;Key Laboratory of Space Launching Site Reliability Technology;;Deep learning for fault diagnosis:The state of the art and challenge[J];控制与决策;2017-08
4 Jia Jinglong;Yu Tao;Wu Zijie;Cheng Xiaohua;School of Electric Power,South China University of Technology;;Fault diagnosis method of transformer based on convolutional neural network[J];电测与仪表;2017-13
5 ZHOU Kai;ZHAO Shilin;HE Min;CHEN Zelong;LI Jiahan;School of Electrical Engineering and Information, Sichuan University;;An Oscillating Wave Test Method Based on Traveling Wave Characteristics of Partial Discharges for Defect Location in Short Cables[J];电网技术;2017-06
6 Lü Shouguo;CUI Yuxin;FENG Yingchun;GUAN Youwei;State Grid Shandong Electric Power Maintenance Company;Shandong Shanda Century Technology Company;;Transformer fault diagnosis method based on comprehensive analysis and its software development[J];电力系统保护与控制;2017-10
7 XU Yongpeng;YANG Fengyuan;QIAN Yong;SHENG Gehao;LI Zhe;JIANG Xiuchen;Department of Electrical Engineering, Shanghai Jiao Tong University;;Pattern Recognition of PD in DC Cable Terminal Joint Based on the Improved ECOC Classifier[J];中国电机工程学报;2017-04
8 JIA Yafei;ZHU Yongli;LAN Zhikun;WANG Liuwang;State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources,North China Electric Power University;State Grid Hebei Baoding Electric Power Company;;Pattern Recognition on Partial Discharge Signals of Transformers Based on S-transform and Deep Belief Network[J];广东电力;2017-01
9 Xiong Qing;Zhu Lingyu;Ji Shengchang;Li Simeng;Zhong Lipeng;Wang Lin;State Key Laboratory of Electrical Insulation and Power Equipment,Xi'an Jiaotong University;School of Electrical Engineering,Shandong University;China Electric Power Research Institute;;Review on Partial Discharge of Oil-paper Insulation Under DC Voltage and Compound Voltage[J];绝缘材料;2017-01
10 GUO Hongying;Fujian Electric Power Engineering Co.,Ltd.;;Diagnostic method of transformer insulation aging based on the recovery voltage polarization spectrum' wavelet packet transform[J];电力系统保护与控制;2016-24
【Secondary Citations】
Chinese Journal Full-text Database 10 Hits
1 GU Xueping;ZHAO Baobin;LIU Wenxuan;Department of Electrical Engineering ,North China Electric Power University;;Load restoration based on multi-objective optimization and grey incidence decision-making[J];电力自动化设备;2015-09
2 XUE Haoran;ZHANG Keheng;LI Bin;PENG Chenhui;NARI Group Corporation (State Grid Electric Power Research Institute);China Real Time Database Co.,Ltd.;;Fault diagnosis of transformer based on the cuckoo search and support vector machine[J];电力系统保护与控制;2015-08
3 WEI Zhen;QI Bo;ZUO Jian;LI Chengrong;Beijing Key Laboratory of High Voltage & EMC (North China Electric Power University);State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources(North China Electric Power University);;A Method to Diagnose Defects in Oil-Paper Insulation of Converter Transformer Based on Image Feature of Partial Discharge[J];电网技术;2015-04
4 CHEN Cui-Ping;Department of Computer Science and Technology, Tongji University;;Text Categorization Based on Deep Belief Network[J];计算机系统应用;2015-02
5 SUN Wenxing;LI Zhaohui;CHENG Shijie;State Key Laboratory of Advanced Electromagnetic Engineering and Technology(Huazhong University of Science and Technology);;A Real Time On-line Method for Automatic Signal Feature Recognition of Fault Discharge in Generator and Its Application[J];电网技术;2015-02
6 XU Zhengyu;CHENG Huanchao;LI Jinzhong;GUAN Jianxin;ZHAO Zhigang;LI Guangfan;China Electrical Power Research Institute;;Real Time Test System and Method of Conduction Characteristics of Oil-paper Insulation[J];高电压技术;2015-01
7 GONG Maofa;ZHANG Yanpan;LIU Yanni;WANG Zhiwen;LIU Lijuan;School of Electrical Engineering and Automation, Shandong University of Technology;Laiwu Power Supply Company, Shandong Electric Power Corporation;Yanshan University;;Fault diagnosis of power transformers based on back propagation algorithm evolving fuzzy Petri nets[J];电力系统保护与控制;2015-03
8 LIU Wenxia;XU Xiaobo;ZHOU Xi;North China Electric Power University;;Daily load forecasting based on SVM for electric bus charging station[J];电力自动化设备;2014-11
9 ZHOU Peihong;HE Huiwen;DAI Min;WAN Lei;China Electric Power Research Institute;;Selection of Arresters Arrangement, Parameters and Apparatuses Insulation Levels for ±1100 kV DC Converter Station[J];高电压技术;2014-09
10 Lü Qi;Dou Yong;Niu Xin;Xu Jiaqing;Xia Fei;National Laboratory for Parallel and Distributed Processing,School of Computer,National University of Defense Technology;Electronic Engineering College,Naval University of Engineering;;Remote Sensing Image Classification Based on DBN Model[J];计算机研究与发展;2014-09
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved