极端气象条件下配电网大范围停电贝叶斯网络建模和停电概率预测方法
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摘要
极端气象事件可引发配电网大面积停电事故,造成严重的经济损失。合理评估和预测极端灾害引发停电风险是实施应急预防所需的关键技术。区别于针对单一设备的灾害停运模型,文章考虑灾后设备停运事件的时空相关性,利用历史灾损记录和灾害数值模拟数据,构建灾害时间贝叶斯网络模型,进而根据灾情快速推理配电网停电范围和停电概率。以IEEE 14节点系统为测试对象,通过仿真算例验证了所提方法的有效性和正确性。
Extreme weather events can cause large-scale power outages in distribution networks,causing serious economic losses.Reasonable assessment and prediction of the risk of blackouts caused by extreme disasters are the key technologies needed to implement emergency prevention.Different from the disaster outage model for single equipment,this paper considers the spacetime correlations of post-disaster equipment outage events,uses historical damage records and disaster numerical simulation data to construct a Bayesian network model,and then quickly inferring the power outage range and power outage probability of distribution network according to the disaster situation.The modified IEEE 14-node system is used as the test system.The effectiveness and correctness of the proposed method are verified by simulation examples.
Extreme weather events can cause large-scale power outages in distribution networks,causing serious economic losses.Reasonable assessment and prediction of the risk of blackouts caused by extreme disasters are the key technologies needed to implement emergency prevention.Different from the disaster outage model for single equipment,this paper considers the spacetime correlations of post-disaster equipment outage events,uses historical damage records and disaster numerical simulation data to construct a Bayesian network model,and then quickly inferring the power outage range and power outage probability of distribution network according to the disaster situation.The modified IEEE 14-node system is used as the test system.The effectiveness and correctness of the proposed method are verified by simulation examples.
引文
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[19]COOPER G F,HERSKOVITS E.A Bayesian method for the induction of probabilistic networks from data[J].Machine Learning,1992,9(4):309-347.
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[21]中国中央气象台台风网V3[DB/OL][.2019-04-23].http://typhoon.nmc.cn/web.html.
[2]CALLAWAY D S,NEWMAN M E J,STROGATZ S H,et al.Network robustness and fragility:percolation on random graphs[J].Physical Review Letters,2000,85(25):5468.
[3]YUAN W,WANG J H,QIU F,et al.Robust optimization-based resilient distribution network planning against natural disasters[J].IEEE Transactions on Smart Grid,2016,7(6):2817-2826.
[4]COMES T,VAN de Walle B.Measuring disaster resilience:the impact of hurricane sandy on critical infrastructure systems[J].ISCRAM,2014(11):195-204.
[5]GUIKEMA S D,NATEGHI R,QUIRING S M,et al.Predicting hurricane power outages to support storm response planning[J].IEEE Access,2014(2):1364-1373.
[6]张鼎华,李嘉莉,孔云茹.台风灾害下广东省沿海城市生命线系统安全规划研究[J].广州大学学报(社会科学版),2018,17(2):27-33.ZHANG Dinghua,LI Jiali,KONG Yunru.A research on urban lifeline systems planning on coastal cities of Guangdong province under the background of typhoon[J].Journal of Guangzhou University(Social Science Edition),2018,17(2):27-33.
[7]冯杰星.电力应急管理体系探索[J].机电信息,2017(36):175-176.
[8]胡娅敏,宋丽莉,刘爱君,等.近58年登陆我国热带气旋的气候特征分析[J].中山大学学报(自然科学版),2008,47(5):115-121.HU Yamin,SONG Lili,LIU Aijun,et al.Analysis of the climatic features of landfall tropical cyclones in China during the past 58 years[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2008,47(5):115-121.
[9]BILLINTONR,SINGHGD.Reliabilityassessmentof transmission and distribution systems considering repair in adverse weather conditions[C]//IEEE CCECE2002,Canadian Conference on Electrical&Computer Engineering,2002,Winnipeg,Man.,Canada.
[10]WANG P,BILLINTON R.Reliability cost/worth assessment of distribution systems incorporating time-varying weather conditions and restoration resources[J].IEEE Transactions on Power Delivery,2002,17(1):260-265.
[11]XIAO F,MCCALLEY J D,OU Y,et al.Contingency probability estimation using weather and geographical data for on-line security assessment[C]//2006 International Conference on Probabilistic Methods Applied to Power Systems,June 11-15,2006,Stockholm,Sweden.
[12]ZHOU Y J,PAHWA A,DAS S.Prediction of weather-related failures of overhead distribution feeders[J].Probability in the Engineering and Informational Sciences,2006,20(1):117-125.
[13]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.
[14]LIU Y,SINGH C.Evaluation of hurricane impact on failure rate of transmission lines using fuzzy expert system[C]//2009 15th International Conference on Intelligent System Applications to Power Systems,November 8-12,2009,Curitiba,Brazil:1-6.
[15]田超.电力系统灾变及其影响的预测预警与智能决策技术研究[D].北京:清华大学,2008.
[16]LIU Y,SINGH C.A methodology for evaluation of hurricane impact on composite power system reliability[J].IEEE Transactions on Power Systems,2011,26(1):145-152.
[17]TSAI C J,LEE C I,YANG W P.A discretization algorithm based on class-attribute contingency coefficient[J].Information Sciences,2008,178(3):714-731.
[18]GARCóA S,FERNóNDEZ A,LUENGO J,et al.Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining:Experimental analysis of power[J].Information Sciences,2010,180(10):2044-2064.
[19]COOPER G F,HERSKOVITS E.A Bayesian method for the induction of probabilistic networks from data[J].Machine Learning,1992,9(4):309-347.
[20]HECKERMAND,GEIGERD,CHICKERINGDM.Learning Bayesian networks:the combination of knowledge and statistical data[J].Machine Learning,1995,20(3):197-243.
[21]中国中央气象台台风网V3[DB/OL][.2019-04-23].http://typhoon.nmc.cn/web.html.