基于联合分布适配的智能电表运行状态评价

许丹; 李游; 李贺龙

doi:10.13700/j.bh.1001-5965.2020.0621

基于联合分布适配的智能电表运行状态评价

doi: 10.13700/j.bh.1001-5965.2020.0621

许丹^1, ,,
李游¹,
李贺龙²

1.
北京航空航天大学可靠性与系统工程学院, 北京 100083
2.
中国电力科学研究院有限公司, 北京 100085

基金项目:

国家自然科学基金 51875017

中央高校基本科研业务费专项资金 YWF-20-BJ-J-726

详细信息

通讯作者:
许丹, E-mail: xudan@buaa.edu.cn

中图分类号: TM744;TP312
计量
- 文章访问数: 431
- HTML全文浏览量: 196
- PDF下载量: 26
- 被引次数: 0
出版历程
- 收稿日期: 2020-11-09
- 录用日期: 2021-06-11
- 网络出版日期: 2022-04-20

Operating status evaluation of smart electricity meters based on joint distribution adaption

XU Dan^{1
, ,},
LI You¹,
LI Helong²

1.
School of Reliability and Systems Engineering, Beihang University, Beijing 100083, China
2.
China Electric Power Research Institute Co., Ltd., Beijing 100085, China

Funds:

National Natural Science Foundation of China 51875017

the Fundamental Research Funds for the Central Universities YWF-20-BJ-J-726

More Information

Corresponding author: XU Dan, E-mail: xudan@buaa.edu.cn

摘要

摘要:
针对智能电表运行状态评价中经常存在带标签的数据采集困难且不同地区的数据分布不一致的问题，在智能电表的运行状态评价领域引入迁移学习中的联合分布适配（JDA）算法。该算法寻找一个最优化的变换矩阵，使得在变换后的空间中不同地区数据的边缘分布和条件分布距离最小化。针对条件分布适配中目标域没有数据标签的问题，采用伪标签迭代的方法使得目标域伪标签不断接近真实标签。变换后空间数据中训练得到的分类模型可以运用于新的地区，实现迁移。实验结果证明了JDA算法在智能电表运行状态评价中的有效性。
- 联合分布适配(JDA) /
- 智能电表 /
- 运行状态评价 /
- 迁移学习 /
- 智能电网
Abstract:
In the operation status evaluation of smart electricity meters, it is often difficult to collect labeled data and the data distribution in different regions is inconsistent. To solve this problem, we introduce the joint distribution adaption (JDA) method in transfer learning in the field of operation status evaluation of smart electricity meters. This method tries to find an optimized transformation matrix to minimize the distance between edge distribution and conditional distribution in different regions in the transformed space. In order to solve the problem that there is no data label in the target domain when the conditional distribution adaptation is made, the JDA method uses pseudo-tag iteration method to make the target domain pseudo-label constantly approach the real tag. The classification model trained from the data in the post-transformation space can be applied to the new region to realize the transfer. The experimental results demonstrate the effectiveness of JDA method in the operation status evaluation of smart electricity meters.
- joint distribution adaption (JDA) /
- smart electricity meters /
- operation status evaluation /
- transfer learning /
- smart grid

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图 1 研究线路

Figure 1. Procedure of study circuit

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图 2 JDA算法流程

Figure 2. JDA algorithm flowchart

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图 3 JDA算法分类误差随迭代次数变化

Figure 3. Classification error of JDA algorithm varies with number of iterations

下载: 全尺寸图片幻灯片

表 1 福建省电表数据集

Table 1. Dataset of electricity meters in Fujian province

属性	取值范围
有功准确度等级	0.5S, 1.0, 2.0
额定负载点误差测量	-0.054~0.25
最小负载点误差测量	-0.249~0.25
最大负载点误差测量	-0.265~0.25
第一次运行误差测量	0.05~0.4
第二次运行误差测量	0~0.45
第三次运行误差测量	0~0.5
运行时间	0~6.5
运行环境	1, 1.2, 2
家族缺陷	1
电量异常次数	0
时钟异常次数	0~7
运行等级	1, 2, 3

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表 2 福建省电表等级分布

Table 2. Grade distribution of electricity meters in Fujian province

运行等级	数量	占比/%
1	13	2.56
2	418	82.44
3	76	15.00

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表 3 基于SMOTE算法不平衡数据处理后的福建省数据分布

Table 3. Data distribution of Fujian province after imbalanced data processing based on SMOTE algorithm

运行等级	数量	占比/%
1	391	35.26
2	418	37.69
3	300	27.05
合计	1 109	100.00

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表 4 目标域数据初始伪标签分布

Table 4. Initial pseudo-label distribution of target domain data

运行等级	数量	占比/%
1	49	8.13
2	369	61.19
3	185	30.68
合计	603	100.00

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表 5 迁移后模型的混淆矩阵

Table 5. Confusion matrix of transferred model

混淆矩阵		预测类1	预测类2	预测类3
真实类	1	39	3	0
真实类	2	10	364	0
真实类	3	0	2	185

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表 6 迁移后模型的主要评价指标

Table 6. Main evaluation indicators of transferred model

评价指标	结果
准确度	0.975
F₁分数	0.943

下载: 导出CSV

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