| 引用本文: | 方 静,彭小圣,刘泰蔚,等.电力设备状态监测大数据发展综述[J].电力系统保护与控制,2020,48(23):176-185.[点击复制] |
| FANG Jing,PENG Xiaosheng,LIU Taiwei,et al.Development trend and application prospects of big data-based conditionmonitoring of power apparatus[J].Power System Protection and Control,2020,48(23):176-185[点击复制] |
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| 电力设备状态监测大数据发展综述 |
| 方静,彭小圣,刘泰蔚,陈玉竹,李文泽,文劲宇,熊磊,王浩鸣 |
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| (1.国网天津市电力公司电缆分公司，天津 300170;2.强电磁工程与新技术国家重点实验室(华中科技大学), 湖北
武汉 430074;3.国网湖北省电力公司襄阳供电公司，湖北 襄阳 441000;4.国网天津市电力公司，天津 300010) |
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| 摘要: |
| 论述了大数据在电力设备状态监测上的发展趋势与应用前景。首先分析了状态监测数据的大数据特征，并从大数据技术、大数据思想方法、大数据算法三个层面论述了大数据对电力设备状态监测的提升点。其次给出了基于大数据的电力设备状态监测系统架构，并从数据采集、数据去噪、特征提取、模式识别、知识挖掘、数据可视化几个方面论述了大数据与状态监测各个环节的结合点。最后通过一个综合监测系统案例，分析了大数据在多源异构数据融合、综合分析与诊断、设备故障预测上的应用。大数据在电力设备状态监测上的深入应用，有利于解决设备状态评价和故障预测的难题，推动该领域朝着更加智能化的方向发展。 |
| 关键词: 大数据 状态监测 关键技术 发展趋势 应用前景 |
| DOI：DOI: 10.19783/j.cnki.pspc.200050 |
| 投稿时间：2020-01-13修订日期：2020-04-16 |
| 基金项目:国家自然科学基金项目资助(51807072)；国网天津市电力公司电力科学研究院项目资助(sgtjdk00pjjs 1800092) |
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| Development trend and application prospects of big data-based conditionmonitoring of power apparatus |
| FANG Jing,PENG Xiaosheng,LIU Taiwei,CHEN Yuzhu,LI Wenze,WEN Jinyu,XIONG Lei,WANG Haoming |
| (1. State Grid Tianjin Electric Power Company Cable Branch, Tianjin 300170, China; 2. State Key Laboratory of
Advanced Electromagnetic Engineering and Technology (Huazhong University of Science and Technology),
Wuhan 430074, China; 3. Xiangyang Power Supply Company, State Grid Hubei Electric Power Company,
Xiangyang 441000, China; 4. State Grid Tianjin Electric Power Company, Tianjin 300010, China) |
| Abstract: |
| This paper presents the development trend and application prospects of big data-based condition monitoring of power apparatus. Based on the analysis of big data attributes of condition monitoring data, three big data application directions of condition monitoring system are discussed, including big data technologies, theories and algorithms. A universal architecture of a big data-based condition monitoring system is presented. Data acquisition, data denoising, feature extraction, pattern recognition and data mining are discussed in detail to present the relationships of big data and all procedures of a condition monitoring system. An application example of a comprehensive condition monitoring system is presented, including big data-based data fusion, condition evaluation and failure-prediction. The application of big data based condition monitoring has great potential to overcome the challenges of condition evaluation and failure prediction and will contribute to the development of intelligent condition monitoring systems of power apparatus.
This work is supported by National Natural Science Foundation of China (No. 51807072). |
| Key words: big data condition monitoring key technologies development prospects application fields |
