基于云服务器的地磁感应电流监测系统的设计

乔珺; 刘青; 高兵; 李艾娣

doi:10.11884/HPLPB201931.190054

基于云服务器的地磁感应电流监测系统的设计

doi: 10.11884/HPLPB201931.190054

西安科技大学电气与控制工程学院, 西安 710054

基金项目:

国家重点研发计划项目 2016YFC0800100

详细信息

作者简介:
乔珺(1992—), 女, 硕士研究生, 研究方向为电网安全运行, qiao_xust17@163.com

中图分类号: TP391
计量
- 文章访问数: 1349
- HTML全文浏览量: 444
- PDF下载量: 69
- 被引次数: 0
出版历程
- 收稿日期: 2019-02-28
- 修回日期: 2019-05-31
- 刊出日期: 2019-07-15

Design of geomagnetic induction current monitoring system based on cloud server

School of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

摘要

摘要: 地磁暴引起的地磁感应电流(GIC) 可能引起变压器直流偏磁, 对电网的安全稳定运行带来威胁, 远程实时监测GIC对电网的GIC防御具有重要的指导意义。设计了一种基于云服务器的电网GIC远程监测系统, 数据采集终端实时采集变压器中性点的GIC, 多监测点数据经GPRS分端口发送至云服务器的内网进行存储, 用户可通过云服务器的公网IP远程访问并对数据进行绘图、下载等处理, 实现了电网GIC数据的实时发布与共享。结合空间天气的预测数据, 还可以初步实现GIC的预警。对系统的数据采集终端以及基于云服务器的监测软件平台两大模块进行了实验室及变电站现场测试, 测试结果表明该系统实现了设计要求, 满足功能需求。
- 云服务器 /
- GIC /
- 霍尔传感器 /
- 实时监测 /
- 预警
Abstract: The geomagnetic induced current (GIC) caused by geomagnetic storm may result in DC bias of the transformer, which poses threats to the safe and stable operation of the power grid.Remote real-time monitoring of GIC can provide important reference for grid GIC defense.Based on cloud server, a GIC monitoring system is designed.The data acquisition terminal collects the transformer's neutral point GIC in real time.Using GPRS, the data from multiple monitoring points are sent by different ports to the internal network of the cloud server for storage.It is possible for users to access the data remotely through the public network IP of the cloud server and process data via drawing and downloading.The real-time release and sharing of GIC data has been realized via this system.Combined with the forecast data of space weather, the early warning of GIC is preliminarily realized.Tests of the data acquisition terminal of system and the monitoring software platform based on the cloud server in the laboratory and substation field show that the system has achieved the design requirement and meets the functional requirements.
- cloud server /
- GIC /
- Hall sensor /
- real-time monitoring /
- early warning

HTML全文

图 1 电网GIC监测总结构图

Figure 1. General structure of the grid GIC monitoring system

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图 2 开环式霍尔电流传感器

Figure 2. Open loop Hall current sensor

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图 3 霍尔传感器性能测试台

Figure 3. Hall sensor performance test bench

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图 4 交流输入下，霍尔传感器的测试数据

Figure 4. Output waveform of Hall element upon AC input

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图 5 调理电路设计

Figure 5. Design of conditioning circuit

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图 6 调理电路仿真结果图

Figure 6. Simulation result diagram of conditioning circuit

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图 7 数据采集终端性能测试

Figure 7. Data acquisition terminal performance test

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图 8 服务器实时数据接收测试

Figure 8. Server real-time data receiving test

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图 9 数据采集终端与服务器联合调试

Figure 9. Data acquisition terminal and server joint debugging

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图 10 数据采集终端现场安装图

Figure 10. Field installation and client display of data acquisition terminal

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表 1 直流输入下霍尔传感器的测试数据

Table 1. Test data of Hall element on DC input

U/V	I/A	U′/V	(I/U′)/(A·V^-1)
0	0	－0.055	0
2.2	0.49	0.246	1.992
4.7	1.00	0.488	2.049
7.1	1.50	0.760	1.974
9.5	2.00	1.013	1.974
11.8	2.50	1.248	2.003
14.2	3.00	1.524	1.984
16.5	3.50	1.762	1.986
18.7	4.00	2.041	1.960
21.0	4.50	2.300	1.957

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表 2 交流输入下霍尔传感器的测试数据

Table 2. Test data of Hall element on AC input

U/V	I/A	U′/V	U″/V	(I/U′)/(A·V^-1)
0	0	0	0	0
5.0	1.0	1.52	1.07	0.935
10.0	2.0	3.12	2.21	0.905
15.0	3.0	4.64	3.28	0.915
20.0	4.0	6.20	4.28	0.935
25.0	5.0	7.60	5.27	0.949
30.0	6.0	9.60	6.69	0.896
30.5	6.1	9.80	6.90	0.884
31.0	6.2	10.00	7.01	0.884
31.5	6.3	10.00	7.05	0.894
32.0	6.4	10.20	7.21	0.887
32.5	6.5	10.40	7.36	0.883

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表 3 传统服务器与云服务器对比

Table 3. Comparison of traditional servers and cloud servers

	traditional server	cloud server
safety	purchase of expensive black hole equipment	effective protection against DDoS attacks
operational	self-supply of your own operating system	built-in operating system
economic	one-time immense investment	on-demand purchase
maintenance	professional and expensive post-maintenance	little maintenance
disaster tolerance	self-repair when there is data corruption	fast and automatic fault recovery
expansibility	lack of flexibility in service environment	rapid business deployment and scale flexibility

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