基于故障因子的用电采集设备状态评估方法

唐冬来; 李擎宇; 周强; 龚奕宇; 谢飞; 周朋; 康乐

doi:10.19421/j.cnki.1006-6357.2024.09.011

基于故障因子的用电采集设备状态评估方法

1. 四川思极科技有限公司;
2. 国网四川省电力公司;
3. 国网信息通信产业集团有限公司

基金项目:

国家重点研发计划项目(2019YFB2103000)~~

详细信息

作者简介:
唐冬来(1980—),男,学士,高级工程师,研究方向为配电网量测、分析、电力系统及其自动化、电子技术等。李擎宇(1995—),男,硕士,工程师,研究方向为电网调度、电力系统及其自动化

通讯作者:
周强(1988—),男,硕士,工程师,研究方向为云计算、电力大数据应用、企业管理信息化咨询等

中图分类号: TM73
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出版历程
- 收稿日期: 2023-12-28
- 刊出日期: 2024-09-04

State evaluation method of electric energy data acquire equipment based on fault factors

1. Sichuan SGIT Technology Co.,Ltd.;
2. State Grid Sichuan Electric Power Company;
3. State Grid Information and Communication Industry Group Co.,Ltd.

Funds:

Supported by National Key R&D Program of China(2019YFB 2103000)

摘要

摘要: 用电采集设备是指对配电变压器、终端用户等电耗数据进行采集的装置，其运行状态将直接影响到客户用电数据采集的稳定性。为解决用电采集设备运行状态评估中，因缺少异常原因追溯分析而导致评估准确率低的问题，提出了一种基于故障因子的用电采集设备状态评估方法。首先，采用隐马尔可夫模型提取用电采集设备评估规则制度中的红线信息，并对用电采集设备数据进行预处理，消除异常数据对评估带来的影响；其次，通过对用电采集设备异常原因进行密度聚类，形成用电采集设备运行状态评估指标和故障影响因子权重，并进行评估；最后，在某省计量中心进行了验证，其运行状态评估准确率为99.56%。所提方法能有效提高用电采集设备的评估准确率。
- 故障因子 /
- 用电采集设备 /
- 状态评估 /
- 隐马尔可夫 /
- 密度聚类 /
- 评估指标
Abstract: Electric energy data acquire equipment（EDA） refers to devices that collect electricity consumption data from distribution transformers, end-users, and other devices. Its operating status will directly affect the stability of customer electricity consumption data collection. To solve the problem of low accuracy in the evaluation of the operation status of EDA due to the lack of abnormal cause tracing analysis, a fault factor based method for evaluating the status of EDA is proposed. Firstly, a hidden Markov model is used to extract the red line information in the evaluation rules and regulations of EDA, and the data of EDA is preprocessed to eliminate the impact of abnormal data on the evaluation; secondly, density clustering is performed on the abnormal causes of EDA to form evaluation indicators for the operation status of EDA and the weight of fault impact factors, and evaluation is carried out. Finally, it is validated at a metrology center in a certain province, and the accuracy of its operational status evaluation is 99.56%. The proposed method can effectively improve the evaluation accuracy of electricity collection equipment.
- fault factors /
- electric energy data acquire equipment(EDA) /
- state evaluation /
- hidden Markov /
- density clustering /
- evaluation indicators

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