Infrared Technology-Based Sensor Data Analysis for Thermal Fault Identification of Electrical Equipment in Intelligent Substations

被引:0
|
作者
Wang, Xuan [1 ]
Zhang, XiaoFeng [2 ]
Zhou, Feng [3 ]
Xu, Xiang [3 ]
机构
[1] Jiaxing Nanhu Univ, Coll Mech & Elect Engn, Jiaxing 314000, Zhejiang, Peoples R China
[2] Beijing Inst Technol, Zhuhai 519088, Guangdong, Peoples R China
[3] Jiaxing Nanhu Univ, Coll Informat Engn, Jiaxing 314000, Zhejiang, Peoples R China
关键词
Infrared radiation;
D O I
10.1155/2022/1710962
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The electrical equipment of intelligent substation cannot deal with the problem of load mutation, and the load adjustment is not timely, which leads to the poor performance of thermal fault identification of electrical equipment. Therefore, a study on thermal fault identification of the electrical equipment of intelligent substation based on infrared technology is proposed. First, under the background of infrared technology, according to the thermal fault feature extraction model of substation electrical equipment, the principle of double station cross location is proposed; combined with the sampling results of thermal fault characteristic parameters of electrical equipment in intelligent substation, the thermal fault characteristics are analyzed; through the process of thermal fault identification of electrical equipment in intelligent substation, the preliminary classification structure of thermal fault is obtained and the thermal fault identification is completed. The experimental results show that the designed method has good performance of thermal fault identification, high output stability, better identification effect after optimization, and highly sensitive identification ability.
引用
收藏
页数:10
相关论文
共 50 条
  • [41] Infrared Thermal Image-Based Sustainable Fault Detection for Electrical Facilities
    Kim, Ju Sik
    Choi, Kyu Nam
    Kang, Sung Woo
    SUSTAINABILITY, 2021, 13 (02)
  • [42] Insulation Defect Detection of Electrical Equipment Based on Infrared and Ultraviolet Photoelectric Sensing Technology
    Gao, Kai
    Lyu, Lijun
    Huang, Hua
    Fu, ChenZhao
    Chen, Fuchun
    Jin, Lijun
    45TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2019), 2019, : 2184 - 2189
  • [43] Two-Level Fault Diagnosis of SF6 Electrical Equipment Based on Big Data Analysis
    Miao, Hongxia
    Zhang, Heng
    Chen, Minghua
    Qi, Bensheng
    Li, Jiyong
    BIG DATA AND COGNITIVE COMPUTING, 2019, 3 (01) : 1 - 18
  • [44] Electrical equipment identification in infrared images based on ROI-selected CNN method
    Han, Sheng
    Yang, Fan
    Yang, Gang
    Gao, Bing
    Zhang, Na
    Wang, Dawei
    ELECTRIC POWER SYSTEMS RESEARCH, 2020, 188
  • [45] Fault diagnosis technology based on multi-sensor data fusion
    Wang, M.
    Wang, W.
    Xiong, C.
    Huang, X.
    Huazhong Ligong Daxue Xuebao/Journal Huazhong (Central China) University of Science and Technology, 2001, 29 (02): : 96 - 98
  • [46] Multi-sensor Data Fusion Technology Based on BP Neural Network Application in the Coal Mine Equipment Fault Diagnosis
    Meng, Xiangzhong
    Liu, Huilong
    Hou, Zisheng
    ADVANCES IN MECHATRONICS AND CONTROL ENGINEERING III, 2014, 678 : 238 - 241
  • [47] Enterprise intelligent manufacturing data analysis technology based on big data analysis
    Wang W.
    Li Q.
    Zhu F.
    International Journal for Simulation and Multidisciplinary Design Optimization, 2024, 15
  • [48] Intelligent target recognition based on the data fusion of radar and infrared imaging sensor
    Zhang, Tie-Zhu
    Jiang, Hong
    Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 2010, 39 (04): : 756 - 760
  • [49] Research on Condition Analysis of Secondary Equipment Based on Electrical Automation Technology
    Yuan, Qi
    2019 5TH INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND MATERIAL APPLICATION, 2020, 440
  • [50] Industrial mechanical equipment fault detection and high-performance data analysis technology based on the Internet of Things
    Ding, Dawei
    INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS, 2024, 18 (04): : 3171 - 3184