CN111612053B - 一种线损率合理区间的计算方法 - Google Patents
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Abstract
Description
配变编号 | 供电所编号 | 供电量 | 用户数 | CONS_NM | TG_CAP | 户均容量 | 单相占比 | SUCC_RATE | 三相下平衡度 | 负荷均匀度 | 线损率 | 线损率计算值 |
43853645 | 134011810 | 196 | 101 | 37 | 400 | 0.0925 | 0.225089 | 100 | 0.85 | 0.67 | 3.26 | 4 |
43855991 | 134011811 | 143 | 134 | 22 | 100 | 0.22 | 0.159069 | 100 | 0.3 | 1 | 6.8 | 6.53 |
43970336 | 134011805 | 422 | 412 | 4 | 100 | 0.04 | 0 | 100 | 0.04 | 1 | 3.95 | 5.13 |
43970339 | 134011813 | 2294 | 2231 | 109 | 250 | 0.436 | 0.528608 | 100 | 0.14 | 0.81 | 2.41 | 3.05 |
127121274 | 134011809 | 830 | 807 | 116 | 315 | 0.368253968 | 0.77617 | 100 | 0.4 | 0.65 | 3.74 | 5.01 |
台区编号 | 合理值区间最小值 | 合理值区间最大值 |
13356392 | 4.05 | 7.29 |
13356379 | 1.9 | 4.71 |
…… | …… | …… |
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CN112288303B (zh) * | 2020-11-05 | 2024-04-23 | 国家电网有限公司 | 确定线损率的方式、装置 |
CN112671096B (zh) * | 2020-11-20 | 2024-02-20 | 浙江华云信息科技有限公司 | 基于数据分析的台区线损电能监测系统及其监测方法 |
CN112488404B (zh) * | 2020-12-07 | 2022-09-23 | 广西电网有限责任公司电力科学研究院 | 一种配电网大规模电力负荷多线程高效预测方法及系统 |
CN113449257A (zh) * | 2021-05-26 | 2021-09-28 | 北京智芯微电子科技有限公司 | 配电网线损的预测方法、控制装置、及存储介质 |
CN113865642A (zh) * | 2021-08-26 | 2021-12-31 | 国网冀北电力有限公司计量中心 | 日线损率异常检测方法、装置、计算机设备及存储介质 |
CN115473216B (zh) * | 2022-05-31 | 2023-06-06 | 云南电网有限责任公司 | 一种改进电网线损计算的方法及系统 |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101178767A (zh) * | 2007-10-18 | 2008-05-14 | 周春光 | 人脸和虹膜混合识别的新方法-识别层融合 |
CN107301499A (zh) * | 2017-05-27 | 2017-10-27 | 天津大学 | 一种基于ami数据的配电馈线统计线损率数据清洗方法 |
CN107340456A (zh) * | 2017-05-25 | 2017-11-10 | 国家电网公司 | 基于多特征分析的配电网工况智能识别方法 |
WO2018120077A1 (zh) * | 2016-12-26 | 2018-07-05 | 江南大学 | 一种基于经验模态分解和决策树rvm的三电平逆变器故障诊断方法 |
CN109977535A (zh) * | 2019-03-22 | 2019-07-05 | 南方电网科学研究院有限责任公司 | 一种线损异常诊断方法、装置、设备及可读存储介质 |
CN110309485A (zh) * | 2019-07-03 | 2019-10-08 | 贵州电网有限责任公司 | 一种基于台区数据特征分类的线损率标杆值计算方法 |
-
2020
- 2020-05-14 CN CN202010405707.6A patent/CN111612053B/zh active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101178767A (zh) * | 2007-10-18 | 2008-05-14 | 周春光 | 人脸和虹膜混合识别的新方法-识别层融合 |
WO2018120077A1 (zh) * | 2016-12-26 | 2018-07-05 | 江南大学 | 一种基于经验模态分解和决策树rvm的三电平逆变器故障诊断方法 |
CN107340456A (zh) * | 2017-05-25 | 2017-11-10 | 国家电网公司 | 基于多特征分析的配电网工况智能识别方法 |
CN107301499A (zh) * | 2017-05-27 | 2017-10-27 | 天津大学 | 一种基于ami数据的配电馈线统计线损率数据清洗方法 |
CN109977535A (zh) * | 2019-03-22 | 2019-07-05 | 南方电网科学研究院有限责任公司 | 一种线损异常诊断方法、装置、设备及可读存储介质 |
CN110309485A (zh) * | 2019-07-03 | 2019-10-08 | 贵州电网有限责任公司 | 一种基于台区数据特征分类的线损率标杆值计算方法 |
Non-Patent Citations (2)
Title |
---|
Yi Zhang等.Runway Visual Range Prediction Based on Ensemble Learning.《 2018 Chinese Automation Congress (CAC)》.2018,全文. * |
赵佩等.基于多维指标数据分析的台区健康智能体检研究设计.《设备研制与应用》.2019,全文. * |
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