基于大数据分析的10 kV配网停电作业时长预测优化研究
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摘要
传统的10 kV配网作业停电时长预测主要依赖于前期现场勘查和工作负责人经验估算。随着配网作业手段的不断进步及用户供电可靠性、优质服务要求的不断提高,对10 kV配网预安排停电预测的精细、准确程度也提出了较高的要求。针对目前停电时长预测与实际完成偏差仍然较大情况,本文提出了一种基于大数据分析的10 kV配网停电作业时长预测方法。通过开展近三年配网停电事件分析,归纳整理影响预测准确度的不同因素,从标准化作业时长典型基准库、标准化作业时长优化调整机制、标准化作业时长后评估体系以及综合停电时长预测模型四个方面展开深入研究,优化提升综合停电作业时长准确预测能力,为停电作业预测偏差管控提供一个可参考、易操作的高效辅助工具。生产作业实际应用表明,这种基于大数据分析的10 kV配网停电作业计划时长预测方法,可以较好的提升停电作业时长准确预测能力,降低可靠性预测偏差,发挥可靠性目标管控作用,助力停电信息发布等优质服务工作精准开展。
The traditional prediction of power outage of operation time in 10 kV distribution network mainly depends on the early site investigations and experience estimation of the person in charge of the work. With the continuous progress of distribution network operation means and the continuous improvement of power supply reliability and quality service requirements of users,higher requirements are put forward for the fine and accurate prediction of 10 kV distribution network outage. In view of the large deviation between the current outage time prediction and the actual completion,this paper presents a method on the prediction of power outage duration of 10 kV distribution network based on big data analysis. Through the analysis of power outages in the past three years,the different factors affecting the accuracy of prediction are summarized,from the typical benchmark library of standardized operation time,the optimization adjustment mechanism of standardized operation time,the post-standardization evaluation system and the comprehensive power outage prediction model. In-depth research of four aspects will be carried out to optimize and improve the accurate prediction ability of the comprehensive power outage operation time,and provide a reference and easy-to-operate high-efficiency auxiliary tool for the predictive deviation control of power outage operations. The practical application of production operation shows that this 10 kV distribution network power outage planning time prediction method based on big data analysis can better improve the accurate prediction ability of power outage operation time,reduce the reliability prediction deviation,and play the role of reliability target management and control to help power outage. Excellent service work such as information release is carried out accurately.
The traditional prediction of power outage of operation time in 10 kV distribution network mainly depends on the early site investigations and experience estimation of the person in charge of the work. With the continuous progress of distribution network operation means and the continuous improvement of power supply reliability and quality service requirements of users,higher requirements are put forward for the fine and accurate prediction of 10 kV distribution network outage. In view of the large deviation between the current outage time prediction and the actual completion,this paper presents a method on the prediction of power outage duration of 10 kV distribution network based on big data analysis. Through the analysis of power outages in the past three years,the different factors affecting the accuracy of prediction are summarized,from the typical benchmark library of standardized operation time,the optimization adjustment mechanism of standardized operation time,the post-standardization evaluation system and the comprehensive power outage prediction model. In-depth research of four aspects will be carried out to optimize and improve the accurate prediction ability of the comprehensive power outage operation time,and provide a reference and easy-to-operate high-efficiency auxiliary tool for the predictive deviation control of power outage operations. The practical application of production operation shows that this 10 kV distribution network power outage planning time prediction method based on big data analysis can better improve the accurate prediction ability of power outage operation time,reduce the reliability prediction deviation,and play the role of reliability target management and control to help power outage. Excellent service work such as information release is carried out accurately.
引文
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[16]盛银波,仲立军,张利庭,等.基于停电明细数据的配电网可靠性监测与研究[J]浙江电力.2017,36(12):70-74.SHENG Yinbo,ZHONG Lijun,ZHANG Liting,et al.Reliability monitoring and research of distribution networks base on detailed outage data[J].2017,36(12):70-74.
[17]徐涛,李明贞,刘毅刚,等.基于改进分类用户单位停电损失函数法的电力用户缺供电损失分析[J].广东电力,2016,29(01):65-69.XU Tao,LI Mingzhen,LIU Yigang,et al.Analysis on insufficient power supply loss of electricity customers based on improved sector custom unit damage function method[J].Guangdong Electric Power,2016,29(01):65-69.
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[2]刘伟桓.配网检修停电时间模型及在可靠性评估中的应用[D].重庆:重庆大学,2013。
[3]杨涛,黄军凯,许逵,等.基于深度学习的变压器故障诊断方法研究[J].电力大数据,2018,21(06):23-30。YANG Tao,HUANG Junkai,XU Kui,et al.Power transformer fault diagnosis method based on deep learning[J].Power systems and big data,2018,21(06):23-30.
[4]刘冬兰,马雷,刘新,等.基于深度学习的电力大数据融合与异常检测方法[J].计算机应用与软件,2018,35(04):61-64+136.LIU Donglan,MA Lei,LIU Xin,et al.Deep learning based anomaly detection approach for power big data[J].Computer Applications and Software,2018,35(04):61-64+136.
[5]张嵩,刘洋,许芳,等.配电网中大数据的挖掘应用[J].电力大数据,2018,21(02):8-12。ZHANG Song,LIU Yang,XU Fang,et al.Application of big data mining in power distribution network[J].Power systems and big data,2018,21(02):8-12.
[6]耿俊成,张小斐,〗袁少光,等.基于大数据分析的电网设备质量评价[J].电力大数据,2018,21(05):36-40.GENG Juncheng,ZHANG Xiaofei,YUAN Shaoguang,et al.Quality evaluation of power grid equipment based on big data analysis[J].Power Systems and Big Data,2018,21(05):36-40.
[7]曲朝阳,陈帅,杨帆,朱莉.基于云计算技术的电力大数据预处理属性约简方法[J].电力系统自动化,2014,38(08):67-71.QU Zhaoyang,CHEN Shuai,YANG Fan,et al.An attribute reducing method for electric power big data preprocessing based on cloud computing technology[J].Automation of Electric Power Systems,2014,38(08):67-71.
[8]陈树祥,朱洪海,杭雪珍.正确认识贝塞尔公式[J].计量与测试技术,2003,30(01):36-37.CHEN Shuxiang,ZHU Hongmei,HANG Xuemei.Accurate comprehension of the bessel formula[J].Metrology&Measurement Technique,2003,30(01):36-37.
[9]刘毅.人工智能在自动组卷建模中应用研究[J].计算机仿真,2011,28(08):385-388.LIU Yi.Auto-generating test paper based on artificial intelligence[J].Computer Simulation,2011,28(08):385-388.
[10]史玉升,梁书云.神经网络实时诊断与优化模型建模法:人工智能在钻井工程中的应用之二[J].地质与勘探,1999,43(03):49-53.SHI Yusheng,LIANG Shuyun.Real time diagnosis and modeling for optimization model using neural network-The second part of application of artificial intilliencee to drilling engineering[J].Geology and Exploration,1999,43(03):49-53.
[11]朱沛恒.基于果蝇算法优化的概率神经网络在变压器故障诊断中的应用[J].电力大数据,2018,21(06):37-43.ZHU Peiheng.Application of probabilistic neural network with fruit fly optimization algorithm in power transformer fault diagnosis[J].Power systems and big data,2018,21(06):37-43.
[12]刘梓权,王慧芳,曹靖,等.基于卷积神经网络的电力设备缺陷文本分类模型研究[J].电网技术,2018,42(02):644-650.LIU Ziquan,WANG Huifang,CAO Jing,et al.A Classification model of power equipment defect texts based on convolutional neural network[J].Power System Technology,2018,42(02):644-650.
[13]郭威,巴秀玲,马文远,等.基于神经网络的电力系统负荷预测问题研究[J].自动化与仪器仪表,2017,37(10):192-194.GUO Wei,BA Xiuling,MA Wenyuan,et al.Study on power system load forecasting based on Neural Networ[J].Automation&Instrumentation,2017,37(10):192-194.
[14]李历波,王玉瑾,王主丁,等.规划态中压配网供电可靠性评估模型[J].电力系统及其自动化学报,2011,23(03):84-88.LI Libo,WANG Yujin,WANG Zhuding,et al.Two reliability evaluation models for medium voltage distribution networks[J].Proceedings of the CSU-EPSA,2011,23(03):84-88.
[15]吴振华.电力企业信息化项目后评估方法探索[J].企业技术开发,2011,30(15):141-143。WU Zhenhua.The research of post project evaluation method for information systems of power enterprise[J].Technological Development of Enterprise,2011,30(15):141-143.
[16]盛银波,仲立军,张利庭,等.基于停电明细数据的配电网可靠性监测与研究[J]浙江电力.2017,36(12):70-74.SHENG Yinbo,ZHONG Lijun,ZHANG Liting,et al.Reliability monitoring and research of distribution networks base on detailed outage data[J].2017,36(12):70-74.
[17]徐涛,李明贞,刘毅刚,等.基于改进分类用户单位停电损失函数法的电力用户缺供电损失分析[J].广东电力,2016,29(01):65-69.XU Tao,LI Mingzhen,LIU Yigang,et al.Analysis on insufficient power supply loss of electricity customers based on improved sector custom unit damage function method[J].Guangdong Electric Power,2016,29(01):65-69.
[18]李春睿.用户费控停电行为分析[J].电力大数据2017,20(09):42-45.LI Chunrui.Analysis on the behavior of user's power fee control power failure[J].Power systems and big data,2017,20(09):42-45.