配网主站系统的配变运维关键技术分析
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摘要
配网设备在供电环节起着至关重要的作用,设备的状态监测及运维检修直接影响配网供电质量和稳定性,而配网变压器是配网设备中非常重要的部分,其运行可靠性很大程度上决定了电网运行的安全性。供电企业越来越关注供电保障与稳定性,以提升用户的用电质量,而传统的数据分析模式已无法满足规模日益增大的配电网规模。主要针对目前配变在线状态监测功能的状况及配变设备运维的重要性,设计网格化区域管理方式,基于设备主人的运维理念,强化设备主人对现场设备的负荷、环境监控和隐患消除能力,结合负荷预警和智能告警,改善目前处理配变设备异常的被动应对状况。
The distribution network equipment plays a crucial role in the power supply link. The condition monitoring and maintenance of the equipment directly affect the quality and stability of the power distribution network. The distribution transformer is a very important part of the distribution network equipment,whose operational reliability largely determines the safety of the grid operation. Recently,power supply companies are paying more and more attention to the power supply security and stability so as to improve the power quality of users. However,the traditional data analysis models are hardly able to meet requirements of the increasing scale of distribution networks. This article focuses on the current status of distribution transformer online monitoring,as well as the importance of equipment operation and maintenance,and proposes a concept of operation and maintenance based on the device ownership,and designs a gridbased regional management model to strengthen the equipment owner's monitoring capability of the field equipment and environment and its elimination ability of hidden dangers. Together with the load warning and intelligent alarm,the proposed method helps to improve the current passive response of the handling of abnormalities in the distribution transformer equipment.
The distribution network equipment plays a crucial role in the power supply link. The condition monitoring and maintenance of the equipment directly affect the quality and stability of the power distribution network. The distribution transformer is a very important part of the distribution network equipment,whose operational reliability largely determines the safety of the grid operation. Recently,power supply companies are paying more and more attention to the power supply security and stability so as to improve the power quality of users. However,the traditional data analysis models are hardly able to meet requirements of the increasing scale of distribution networks. This article focuses on the current status of distribution transformer online monitoring,as well as the importance of equipment operation and maintenance,and proposes a concept of operation and maintenance based on the device ownership,and designs a gridbased regional management model to strengthen the equipment owner's monitoring capability of the field equipment and environment and its elimination ability of hidden dangers. Together with the load warning and intelligent alarm,the proposed method helps to improve the current passive response of the handling of abnormalities in the distribution transformer equipment.
引文
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[23]高赐威,李倩玉,苏卫华,等.短期负荷预测中考虑积温效应的温度修正模型研究[J].电工技术学报,2015,30(4):242-248.GAO Ciwei,LI Qianyu,SU Weihua,et al.Temperature correction model research considering temperature cumulative effect in short-term load forecasting[J].Transactions of China Electrotechnical Society,2015,30(4):242-248.
[2]丁诗洋,夏友森,沈庆.以“设备主人制”为抓手促进变电站精益化管理[J].山东工业技术,2016(22):287.DING Shiyang,XIA Yousen,SHENG Qing.“Equipment master system”as the starting point to promote the substation lean management[J].Shandong Industrial Technology,2016(22):287.
[3]陈维钏.配变低压无功补偿设备状态监测与评估系统的开发研究[D].北京:华北电力大学,2016.
[4]杜凤青.智能变压器综合监测和自我评估方法研究[D].上海:上海交通大学,2013.
[5]哈金才.粗糙集理论在电力系统负荷预测中的应用[D].银川:宁夏大学,2008.
[6]石德琳.基于神经网络的电力负荷预测研究与实现[D].济南:山东大学,2016.
[7]郎坤.电力系统短期负荷预测及经济调度决策优化研究[D].大连:大连理工大学,2016.
[8]雷正新,韩蓓,聂萌,等.配电网大数据环境下的多点负荷预测算法与应用研究[J].电力系统与保护控制,2016,44(23):68-78.LEI Zhengxin,HAN Bei,NIE Meng,et al.Research and application of multi-node load forecasting algorithm under the environment of distribution network’s big data[J].Power System Protection and Control,2016,44(23):68-78.
[9]王德文,孙志伟.电力用户侧大数据分析与并行负荷预测[J].中国电机工程学报,2015,35(3):527-537.WANG Dewen,SUN Zhiwei.Big data analysis and parallel load forecasting of electric power user side[J].Proceedings of CSEE,2015,35(3):527-537.
[10]王圣竹.线路线损及台区线损降损管理[J].企业技术开发,2016,34(30):41.WANG Shengzhu.Analysis of line loss and loss of line line loss and station[J].Science and Technology&Innovation,34(30):41.
[11]张军.关于电网分层分区供电方案的研究[J].中国高新技术企业,2008(24):105.ZHANG Jun.Research on power supply scheme of power grid hierarchical division[J].China High Technology enterprises,2008(24):105
[12]李晓峰,刘越.基于“大检修”体系的输电专业设备主人制探讨[J].湖北电力,2012,36(4):32-33.LI Xiaofeng,LIU Yue.Discussion on the master system of transmission equipment based on“Big Overhaul”system[J].Hubei Electric Power,2012,36(4):32-33.
[13]梁毅.基于BP神经网络的电力负荷预测模型设计与系统实现[D].成都:西华大学,2016.
[14]孟安波,胡函武,刘向东.基于纵横交叉算法优化神经网络负荷预测模型[J].电力系统与保护控制,2016,44(7):102-106.MENG Anbo,HU Hanwu,LIU Xiangdong.Short-term load forecasting using neural network based on wavelets and crisscross optimization algorithm[J].Power System Protection and Control,2016,44(7):102-106.
[15]史步海,朱学峰.LMBP神经网络改进算法的研究[J].控制工程,2008,15(2):34-38.SHI Buhai,ZHU Xuefeng.On improved algorithm of LMBP neural networks[J].Control Engineering,2008,15(2):34-38.
[16]童超,彭穗,薛云涛.基于GM-RBF神经网络的光伏发电功率预测[J].电子设计工程,2015,9(9):45-48.TONG Chao,PENG Sui,XUE Yuntao.Photovoltaic power prediction based on GM-RBF neural network[J].Electronic Design Engineering,2015,9(9):45-48.
[17]杨英,唐平,王越超,等.基于LMBP改进算法的神经网络结构优化[J].计算机工程,2008,34(1):215-217.YANGYing,TANGPing,WANGYuechao,etal.Neuralnetwork structure optimization based on improved LMBP algorithm[J].Computer Engineering,2008,34(1):215-217.
[18]马立新,李渊,郑晓栋,等.基于高阶BP神经网络的日最大负荷预测[J].能源研究与信息,2016,32(3):153-157.MA Lixin,LI Yuan,ZHENG Xiaodong,et al.Daily peak load prediction based on advanced BP neural network[J].Energy Research and Information,2016,32(3):153-157.
[19]钟清,孙闻,余南华,等.主动配电网规划中的负荷预测与发电预测[J].中国电机工程学报,2014,34(19):3050-3056.ZHONG qing,SUN Wen,YU Nanhua,et al.Load and power forecasting in active distribution network planning[J].Proceedings of the CSEE,2014,34(19):3050-3056.
[20]张思远,何光宇,梅生伟,等.基于相似时间序列检索的超短期负荷预测[J].电网技术,2008,32(12):56-59.ZHANG Siyuan,HE Guangyu,MEI Shengwei,et al.Ultra-short term load forecasting based on similarity search in time-series[J].Power System Technology,2008,32(12):56-59.
[21]杨鲁景,武进,石鹏飞.改进BP神经网络预测模型实证分析[J].山西大同大学学报(自然科学版),2015(4):14-16.YANG Lujing,WU jin,SHI Pengfei.Empirical analysis of improved BP neural network prediction model[J].Journal of Shanxi Datong University(Natural Science Edition),2015(4):14-16.
[22]卢宇,陈锦莹,陈静,等.基于LMBP算法的在线学习评价模式研究[J].中国远程教育,2016(3):28-32.LU Yu,CHEN Jinying,Chen Jing,et al.Research on evaluation model of online learning based on LMBP algorithm[J].Distance Education in China,2016(3):28-32.
[23]高赐威,李倩玉,苏卫华,等.短期负荷预测中考虑积温效应的温度修正模型研究[J].电工技术学报,2015,30(4):242-248.GAO Ciwei,LI Qianyu,SU Weihua,et al.Temperature correction model research considering temperature cumulative effect in short-term load forecasting[J].Transactions of China Electrotechnical Society,2015,30(4):242-248.