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电网技术  2018, Vol. 42 Issue (8): 2391-2398    DOI: 10.13335/j.1000-3673.pst.2018.0760
  国家重点研发计划 本期目录 | 过刊浏览 | 高级检索 |
计及天气因素相关性的配电网故障风险等级预测方法
张稳1, 盛万兴2, 刘科研2, 杜松怀1, 贾东梨2, 白牧可2
1.中国农业大学 信息与电气工程学院,北京市 海淀区 100083;
2.中国电力科学研究院有限公司,北京市 海淀区 100192
A Prediction Method of Fault Risk Level for Distribution Network Considering Correlation of Weather Factors
ZHANG Wen1, SHENG Wanxing2, LIU Keyan2, DU Songhuai1, JIA Dongli2, BAI Muke2
1. College of Information and Electrical Engineering, China Agriculture University, Haidian District, Beijing 100083, China;
2. China Electric Power Research Institute, Haidian District, Beijing 100192, China
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摘要 配电网风险等级的准确性预测对配电网运行维护具有重要意义。针对配电网故障影响因素众多且冗余性强的问题,提出一种计及天气因素的配电网故障特征选择和故障停电风险等级预测的方法。通过对故障数据的预处理,归纳出18个配电网故障特征,综合考虑故障发生频率、停电时长和缺供电量比例,确定风险等级划分依据;提出改进G-ReliefF算法实现对故障特征权重计算和冗余剔除,得到最优故障特征集合;基于Adaboost改进C4.5决策树算法进行配电网故障风险等级预测,挖掘故障停电风险等级与天气因素间的关联关系。通过实际算例分析,验证了所提方法的有效性,可以为配电网风险预控提供有效依据。
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张稳
盛万兴
刘科研
杜松怀
贾东梨
白牧可
关键词:  配电网  天气因素  特征选择  相关性  Adaboost  风险预测    
Abstract: It is significant for operation and maintenance of distribution network to forecast risk level more accurately. In allusion to the problems of many factor and strong redundancy related to fault, a method of fault feature selection and fault outage risk level prediction for distribution network considering meteorological factors was proposed. Eighteen fault features of distribution network were summarized after data preprocessing and the basis of risk level was determined after considering failure frequency, proportion of outage duration and power supply. An improved G-ReliefF algorithm was proposed to calculate fault feature weights and eliminate redundancy. Then, an Adaboost based C4.5 decision tree algorithm was used to forecast the risk level of distribution network fault and find relationship between fault outage risk level and meteorological factors. Results of analyzing calculation example showed that the proposed method is effective. It provides an efficient basis for pre-control of distribution network risk.
Key words:  distribution network    weather factor    feature selection    correlation    Adaboost    risk prediction
收稿日期:  2018-04-12                     发布日期:  2018-08-08      期的出版日期:  2018-08-05
ZTFLH:  TM721  
基金资助: 国家重点研发计划项目(2017YFB0903000); 国家电网公司科技项目(52020116000G)
作者简介:  张稳(1988),女,博士研究生,研究方向为电力系统自动化,E-mail:zhangwen1898@126.com;盛万兴(1965),男,博士,教授级高级工程师,研究方向为电力系统自动化、可再生能源发电等;刘科研(1979),男,博士,高级工程师,研究方向为配电网仿真建模与智能分析;杜松怀(1963),男,通信作者,教授,研究方向为电力市场和电力系统继电保护等。
引用本文:    
张稳, 盛万兴, 刘科研, 杜松怀, 贾东梨, 白牧可. 计及天气因素相关性的配电网故障风险等级预测方法[J]. 电网技术, 2018, 42(8): 2391-2398.
ZHANG Wen, SHENG Wanxing, LIU Keyan, DU Songhuai, JIA Dongli, BAI Muke. A Prediction Method of Fault Risk Level for Distribution Network Considering Correlation of Weather Factors. POWER SYSTEM TECHNOLOGY, 2018, 42(8): 2391-2398.
链接本文:  
http://www.dwjs.com.cn/CN/10.13335/j.1000-3673.pst.2018.0760  或          http://www.dwjs.com.cn/CN/Y2018/V42/I8/2391
[1] 徐特威,鲁宗相,乔颖,等.基于典型故障与环境场景关联识别的城市配电网运行风险预警方法[J].电网技术,2017,41(8):2577-2584.
Xu Tewei,Lu Zongxiang,Qiao Ying,et al.A risk warning method for urban distribution network based on associated recognition of typical fault and environment scenario[J].Power System Technology,2017,41(8):2577-2584(in Chinese).
[2] 李蕊,李跃,苏剑,等.配电网重要电力用户停电损失及应急策略[J].电网技术,2011,35(10):170-176.
Li Rui,Li Yue,Su Jian,et al.Power supply interruption cost of important power consumers in distribution network and its emergency management[J].Power System Technology,2011,35(10):170-176(in Chinese).
[3] 马瑞,周谢,彭舟,等.考虑气温因素的负荷特性统计指标关联特征数据挖掘[J].中国电机工程学报,2015,35(1):43-51.
Ma Rui,Zhou Xie,Peng Zhou,et al.Data mining on correlation feature of load characteristics statistical indexes considering temperature[J].Proceedings of the CSEE,2015,35(1):43-51(in Chinese).
[4] 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,2010,35(2):287-293.
Liu Keyan,Sheng Wanxing,Zhang Dongxia,et al.Big data application requirements and scenario analysis in smart distribution network[J].Proceedings of the CSEE,2010,35(2):287-293(in Chinese).
[5] Kankanala P,Das S,Pahwa A.AdaBoost+: an ensemble learning approach for estimating weather-related outages in distribution systems[J].IEEE Transactions on Power Systems,2014,29(1):359-367.
[6] Zhu D,Cheng D,Broadwater R P,et al.Storm modeling for prediction of power distribution system outages[J].Electric Power Systems Research,2006,77(8):973-979.
[7] Liu H,Davidson R A,Rosowsky D V,et al.Negative binomial regression of electric power outages in hurricanes[J].Journal of Infrastructure Systems,2005,11(4):258-267.
[8] Liu H,Davidson R A,Apanasovich T V.Spatial generalized linear mixed models of electric power outages due to hurricanes and ice storms[J].Reliability Engineering and System Safety,2007,93(6):897-912.
[9] Radmer D T,Kuntz P A,Christie R D,et al.Predicting vegetation-related failure rates for overhead distribution feeders[J].IEEE Transactions on Power Delivery,2002,17(4):1170-1175.
[10] Li H,Treinish L A,Hosking J R M.A statistical model for risk management of electric outage forecasts[J].IBM Journal of Research & Development,2010,54(3):8:1-8:11.
[11] Zhou Y,Pahwa A,Yang S S.Modeling weather-related failures of overhead distribution lines[J].IEEE Transactions on Power Systems,2006,21(4):1683-1690.
[12] 孙小军,林圣,冯玎,等.考虑负荷特性的牵引变压器短期风险评估[J].电网技术,2016,40(9):2817-2823.
Sun Xiaojun,Lin Sheng,Feng Ding,et al.Short-time risk evaluation of traction transformer based on loading characteristics[J].Power System Technology,2016,40(9):2817-2823(in Chinese).
[13] 费胜巍,孙宇.融合粗糙集与灰色理论的电力变压器故障预测[J].中国电机工程学报,2008,28(16):154-160.
Fei Shengwei,Sun Yu.Fault prediction of power transformer by combination of rough sets and grey theory[J].Proceedings of the CSEE,2008,28(16):154-160(in Chinese).
[14] 程宝清,韩凤琴,桂中华.基于小波的灰色预测理论在水电机组故障预测中的应用[J].电网技术,2005,29(13):40-44.
Cheng Baoqing,Han Fengqin,Gui Zhonghua.Application of wavelet transform based grey theory to fault forecasting of hydroelectric generating sets[J].Power System Technology,2005,29(13):40-44(in Chinese).
[15] 吴文可,文福拴,薛禹胜,等.基于马尔可夫链的电力系统连锁故障预测[J].电力系统自动化,2013,37(5):29-37.
Wu Wenke,Wen Fushuan,Xue Yusheng,et al.Prediction of chain failure of power system based on markov chain[J].Automation of Electric Power Systems,2013,37(5):29-37(in Chinese).
[16] 李再华,白晓民,周子冠,等.基于特征挖掘的电网故障诊断方法[J].中国电机工程学报,2010,30(10):16-22.
Li Zaihua,Bai Xiaomin,Zhou Ziguan,et al.Method of power grid fault diagnosis based on feature mining[J].Proceedings of the CSEE, 2010,30(10):16-22(in Chinese).
[17] 刁赢龙,盛万兴,刘科研,等.大规模配电网负荷数据在线清洗与修复方法研究[J].电网技术,2015,39(11):3134-3140.
Diao Yinglong,Sheng Wanxing,Liu Keyan,et al.Research on online cleaning and repair methods of large-scale distribution network load data[J].Power System Technology,2015,39(11):3134-3140(in Chinese).
[18] 牛东晓,谷志红,邢棉,等.基于数据挖掘的SVM短期负荷预测方法研究[J].中国电机工程学报,2006,26(18):6-12.
Niu Dongxiao,Gu Zhihong,Xing Mian,et al.Study on forecasting approach to short-term load of SVM based on data mining[J].Proceedings of the CSEE,2006,26(18):6-12(in Chinese).
[19] 胡丽娟,刁赢龙,刘科研,等.基于大数据技术的配电网运行可靠性分析[J].电网技术,2017,41(1):265-271.
Hu Lijuan,Diao Yinglong,Liu Keyan,et al.Operational reliability analysis of distribution network based on big data technology[J].Power System Technology,2017,41(1):265-271(in Chinese).
[20] 张文俊. 配电网故障停电风险评估指标体系及评估方法研究[D].保定:华北电力大学,2014.
[21] 周湶,廖婧舒,廖瑞金,等.含分布式电源的配电网停电风险快速评估[J].电网技术,2014,38(4):882-887.
Zhou Quan,Liao Jingshu,Liao Ruijin,et al.Rapid assessment of power system blackout risk with distributed generation[J].Power System Technology,2014,38(4):882-887(in Chinese).
[22] 蒋玉娇,王晓丹,王文军,等.一种基于PCA和ReliefF的特征选择方法[J].计算机工程与应用,2010,46(26):170-172.
Jiang Yujiao,Wang Xiaodan,Wang Wenjun,et al.New feature selection approach by PCA and ReliefF[J].Computer Engineering & Applications,2010,46(26):170-172(in Chinese).
[23] 王雁凌,吴梦凯,周子青,等.基于改进灰色关联度的电力负荷影响因素量化分析模型[J].电网技术,2017,41(6):1772-1778.
Wang Yanling,Wu Mengkai,Zhou Ziqing,et al.Quantitative analysis model of power load influencing factors based on improved grey relational degree[J].Power System Technology,2017,41(6):1772-1778(in Chinese).
[24] 吴俊利,张步涵,王魁.基于Adaboost的BP神经网络改进算法在短期风速预测中的应用[J].电网技术,2012,36(9):221-225.
Wu Junli,Zhang Buhan,Wang Kui.Application of adaboost-based bp neural network for short-term wind speed forecast[J].Power System Technology,2012,36(9):221-225(in Chinese).
[25] 栗然,刘宇,黎静华,等.基于改进决策树算法的日特征负荷预测研究[J].中国电机工程学报,2005,25(23):36-41.
Li Ran,Liu Yu,Li Jinghua,et al.Study on the daily characteristic load forecasting based on the optimized algorithm of decision tree[J].Proceedings of the CSEE,2005,25(23):36-41(in Chinese).
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