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作 者:陈杰 张廼龙 邱刚 高嵩 CHEN Jie;ZHANG Nailong;QIU Gang;GAO Song(Electric Power Research Institute,State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 211100,China)
机构地区:[1]国网江苏省电力有限公司电力科学研究院,江苏南京211100
出 处:《电力科学与技术学报》2023年第6期267-272,共6页Journal of Electric Power Science And Technology
基 金:国家电网总部科技项目(5500-202118138A-0-0-00)。
摘 要:科学合理地对导线覆冰进行预警评估有助于及时地对冰冻灾害风险做出准确应对措施。提出一种适用于小样本数据库的多因素灰色预测模型GM(1,N),相比于传统神经网络模型,这种模型对建模数据库样本容量要求低,具有较高的建模与计算效率,并且可实时根据气象参数对导线覆冰程度进行预测,从而实现输电线路导线覆冰灾害风险预警的目的。基于所提模型进行案例分析,在工程应用场景中将覆冰程度划分为5个等级,研究发现:基于GM(1,N)灰色理论多因素预测模型对覆冰厚度预测的误差平均在8.1%,输电线路导线覆冰灾害风险预警准确率高达94%。此外,在覆冰厚度临界值附近的预测值增加一定的安全裕度值,可以降低将高风险等级判断为低一级的风险等级的概率。本文所提出的覆冰厚度预测灰色模型可以指导冰区输电线路的抗冰工作。Scientific and reasonable early warning and assessment of conductor icing can help to take accurate response measures to prevent the freezing disaster risk in time.This paper presents a multi factor grey prediction model GM(1,N)suit for small sample database.Compared with the traditional neural network model,the proposed model requires lower sample size of the modeling database and corresponds to higher modeling and calculation efficiency.The degree of conductor icing can be predicted in real time according to meteorological parameters,which can realize the risk warning of transmission line conductor icing disaster.Based on the case analysis of the proposed model,the icing degree is divided into five grades in the engineering application scenario.It is found that the average error of the multi factor prediction model based on GM(1,N)grey theory in ice thickness prediction is 8.1%,and the risk warning accuracy of transmission line icing disaster is as high as 94%.In addition,the probability of judging the high risk level as the lower one can be decreased by adding a certain safety margin value near the critical value of the ice thickness.In the ice area,The application of the ice thickness grey prediction model proposed in this paper can guide the anti-ice work of transmission lines in icing area.
分 类 号:TM852[电气工程—高电压与绝缘技术]
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