| 卢德龙,王巨灏,张 颖,黄馨仪,吴 阳.计及用户侧数据不确定性的台区线损粗糙算法[J].电力需求侧管理,2020,22(6):33-38 |
| 计及用户侧数据不确定性的台区线损粗糙算法 |
| Rough algorithm of station area line loss considering user side data uncertainty |
| 投稿时间：2020-07-09 修订日期：2020-08-17 |
| DOI：10. 3969 / j. issn. 1009-1831. 2020. 06. 008 |
| 中文关键词: 线损 数据不确定 粗糙集理论 |
| 英文关键词: line loss data uncertainty rough set theory |
| 基金项目:国家重点研发计划（2016YFB0900600） |
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| 摘要点击次数: 1703 |
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| 中文摘要: |
| 台区线损治理是电网公司消费侧运营管理的核心议题，也是提质增效保障电网利润的基础。依托安装的电能信息采集终端，电网营销系统采集维护着海量的用户数据，利用实时采集的电网大数据去探知电网的运行情况并进行及时的维护是台区线损治理的有效途径。电网监测采集的数据量虽然庞大，但面对复杂的台区线损不合格原因仍然捉襟见肘。利用已知的采集数据，建立数据与故障原因之间的因果关系链可极大的减轻运维人员的工作压力，提升线损治理水平。海量的监控终端在采集、通信与维护的过程中必然存在着数据的不准确性，提出利用粗糙集理论解决用户侧数据的不确定性，给出了不同故障下的特征数据，分析了故障与数据的内在联系，实现了故障参数特征数据的解耦。给出了导致台区故障原因的分类，并验证了所提出分类方法的正确性。 |
| 英文摘要: |
| The management of station line loss is the core issue of consumption side operation and management of power grid company, and it is also the basis of improving quality and efficiency to guarantee power grid profit. Relying on the power information collection terminal, the power grid marketing system collects and maintains a large number of user data. Using the real time collected power grid big data to detect the operation of the power grid and carry out timely maintenance is an effective way to control the line loss. Although there are a lot of data collected by power grid monitoring, it is still insufficient to face the complicated causes of line loss disqualification. Using the known collection data, establishing the causal link between data and fault causes can greatly reduce the work pressure of operation and maintenance personnel. In the process of data acquisition, communication and maintenance, there must be inaccuracy in massive monitoring terminals. Using rough set theory to solve the uncertainty of user side data is proposed,and the characteristic data under different faults are given. The internal relationship between faults and data are analyzed, and the decoupling of fault parameter characteristic data is realized. The classification of fault causes in the station area is given, and the correctness of the proposed classification method is verified. |
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