基于嵌入式技术的电力数据支持风险控制方法

doi:10.11959/j.issn.1000-0801.2024030

摘要/Abstract

摘要：

为了提高电力系统风险控制中不良数据识别能力和故障定位精度，利用嵌入式技术，设计出电力系统的新型风险控制方法。使用S3C2440芯片作为中央处理器，通过同步向量测量单元（phasor measurement unit， PMU）技术甄别电力系统中出现的不良数据，提高了甄别的效率；在传统的多元宇宙算法基础上进行改进，基于PMU采集的数据进行故障定位，有效提高故障定位精度。在仿真实验中，该方法的不良数据识别效率至少提高33.76%，故障定位的精度提高26.33%，效率至少提高43.4%。所提方法在电力系统中具有良好的风险控制能力。

关键词: 电力系统, 风险控制, 嵌入式, PMU, 数据识别, MVO算法

Abstract:

In order to improve the ability of bad data identification and fault location accuracy in power system risk control, a new risk control method for power systems using embedded technology was designed.S3C2440 chip was used as the central processing unit, the phasor measurement unit (PMU) technology was used to identify bad data in the power system, improving the efficiency of identification.Improved on the basis of traditional multiverse algorithms, fault localization was based on data collected by PMU, effectively improving the accuracy of fault localization.In the simulation experiment, the efficiency of identifying bad data by the proposed method has been improved by at least 33.76%, the accuracy of fault localization has been improved by 26.33%, and the efficiency has been improved by at least 43.4%.The proposed method has good risk control capabilities in the power system.

Key words: power system, risk control, embedded, PMU, data recognition, MVO algorithm

中图分类号:

TP391

杨成月, 于宙, 赵园园, 陈常龙. 基于嵌入式技术的电力数据支持风险控制方法[J]. 电信科学, 2024, 40(2): 141-149.

Chengyue YANG, Zhou YU, Yuanyuan ZHAO, Changlong CHEN. Risk control method for power data support based on embedded technology[J]. Telecommunications Science, 2024, 40(2): 141-149.

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引脚	具体功能
VDD	接3.3 V电压
WP	保护引脚，芯片读写
SCL	时钟输入
SDA	数据存储
A0	接地
A1	接地
A2	接地
GND	接地

估计方法	迭代次数/次	计算时间/ms	平均误差
本文方法	25	181.5	1.73×10^-5
大数据技术	25	274.0	2.90×10^-5
记忆网络	25	336.1	5.88×10^-5

变化幅度	本文方法	大数据	记忆网络
5%	13.5%	16.7%	18.6%
10%	13.1%	16.3%	18.0%
15%	12.6%	16.1%	17.8%
20%	12.3%	15.8%	17.3%
25%	11.9%	15.3%	16.9%

变化幅度	本文方法	大数据	记忆网络
5%	13.5%	16.7%	18.6%
10%	13.1%	16.3%	18.0%
15%	12.6%	16.1%	17.8%
20%	12.3%	15.8%	17.3%
25%	11.9%	15.3%	16.9%

方法	准确率	时间/s
本文方法	97.3%	1.63
GA	59.9%	2.88
IA	73.1%	3.60