刘学军,俞伟,何颋,陈晨,王建炜
LIU Xuejun,YU Wei,HE Ting,CHEN Chen,WANG Jianwei

• 1:国网浙江杭州市富阳区供电有限公司

摘要(Abstract)：

大数据带来的挑战在于已具规模的配电网运行数据具有采样尺度不同、数据断面不同、数据存在误差、数据分散等特点。针对上述特点,首先对多源数据融合中的不良数据进行重点辨识和清洗,然后结合智能配电网不同应用场景,包括运用神经网络算法开展配电网负荷预测,有利于电网经济运行和安全调度;基于最大供电能力原理开展配电网负荷调整,有利于负荷优化再分配;探索低压供电可靠性评估方法以及"站-线-变-户"无功电压分析模型,细化供电质量分析;最后构建配电网供电能力评估指标体系,为配电网规划、安全运行、优质服务等方面提供数据支撑。
The challenges that big data bring to operating data of distribution networks are different sampling scale, data profile, data errors and scattered data. Therefore, bad data in multivariate data fusion must be once again recognized and cleaned; beside, grid load forecast must be conducted via neural network algorithm in accordance with various application scenarios of intelligent distribution networks to achieve operation efficiency and dispatch safety of power grid; load adjustment of distribution networks based on maximum enables supply capacity load optimization and redistribution; a reliability evaluation method of low-voltage power supply and a reactive voltage analysis model of "substation-line-transformer-consumer" are explored to further analyze power supply quality; finally, an indicate system for power supply capacity of distribution networks is established to provide data support for distribution networks planning, operation safety and high-quality services.

关键词(KeyWords)： 大数据;配电网;预警;状态评估
big data;distribution network;early warning;state evaluation

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 刘学军,俞伟,何颋,陈晨,王建炜
LIU Xuejun,YU Wei,HE Ting,CHEN Chen,WANG Jianwei

DOI: 10.19585/j.zjdl.201712015

参考文献(References)：

• [1]MERV ADRIAN.Big data:it's going mainstream and it's yournext opportunity[J].Teradata Magazine,2011,5(1):3-5.
• [2]MANYIKA J,CHUI M,BROWN B,et al.Big data:the next frontier for innovation competition and productivity[R].USA:Mckinsey Global Institute,2011.
• [3]刘科研,盛万兴,张东霞,等.智能配电网大数据应用需求和场景分析研究[J].中国电机工程学报,2015,35(2):287-293.
• [4]潘坚跃,赵海,施婧.多系统监测与大数据分析在供电抢修服务中的应用[J].浙江电力,2015,34(8):59-61.
• [5]刘潇洋,张凡,陆燕芬,等.基于决策理论的配电网负荷预测技术[J].华东电力,2013,41(10):2084-2085.
• [6]DANESHDOOST M,LOTFALIAN M,BUMROONGGIT G,et al.Neural network with fuzzy set based classification for short-term load forecasting[J].IEEE Transactions on Power System,1998,13(4):1386-1391.
• [7]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.
• [8]刘艳红,倪秋龙,黄民翔.多小水电地区网供负荷预测研究[J].浙江电力,2015,34(12):7-10.
• [9]汪卫华.现有配电模式下配网供电能力研究[J].继电器,2007,35(14):13-16.
• [10]李振坤,陈星莺,刘皓明,等.配电网供电能力的实时评估分析[J].电力系统自动化,2009,33(6):36-39.
• [11]欧阳武,程浩忠,张秀彬,等.城市最大供电能力评估法[J].高电压技术,2009,5(2):403-407.
• [12]刘健,司玉芳.考虑负荷变化的配电网架安全评估及其应用[J].电力系统自动化,2011,35(23):70-75.
• [13]吴良建,祝刚,许哲伟,等.考虑全寿命周期的电力设备可靠性分析[J].浙江电力,2015,34(11):61-64.
• [14]王春雷,梁小放,章坚民,等.基于用电采集系统的负荷特性曲线聚类分析[J].浙江电力,2014,33(7):6-10.
• [15]张刘冬,丁昊,袁晓冬,等.考虑价格需求响应的主动配电网动态经济调度[J].电力工程技术,2017,36(4):31-35.
• [16]姚刚,刘速飞,马劲东,等.配电网评估指标体系建立与评估方法[J].华东电力,2009,37(12):2038-2040.