文章摘要
引用本文:张斌,金涛,江岳文,叶荣,温步瀛.组合核极限学习机气象参数预测及输电线最大载流量概率模型的研究[J].福州大学学报(自然科学版),2018,46(6):
组合核极限学习机气象参数预测及输电线最大载流量概率模型的研究
Study on Meteorological Parameter Prediction of Multiple Kernel Limit Learning Machine and Probability Model of Maximum Current Carrying Capacity of Transmission Line
投稿时间:2017-09-13  修订日期:2017-11-06
DOI:
中文关键词: 动态载流量  经验模态分解  核极限学习机  粒子群  概率建模  动态增容
英文关键词: Dynamic current-carrying capacity,  Empirical mode decomposition  Kernel Extreme Learning Machine,  Particle swarm,  Probabilistic modeling,  Dynamic rating
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
作者单位E-mail
张斌 福州大学电气工程与自动化学院 1012225858@qq.com 
金涛 福州大学电气工程与自动化学院  
江岳文 福州大学电气工程与自动化学院  
叶荣 国网福建省电力有限公司经济技术研究院  
温步瀛 福州大学电气工程与自动化学院  
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中文摘要:
      输电线路最大载流量与线路周围的气象参数密切相关。针对气象参数预测的问题,提出了应用经验模态分解法(EMD)对研究地区的历史气象数据按时间序列进行分解和特征提取,应用粒子群优化的核极限学习机(PSO-KELM)进行预测的方法。该气象参数的预测结果,将作为输电线路最大载流量概率模型的源数据,基于载流量密度函数的概率建模,提出了基于气象参数的输电线路最大载流量的计算方法。某地区电网的应用分析结果表明,在用电高峰期时可根据气象参数预测结果动态调整输电线路的载流量,在确保输电线路的安全可靠性的前提下,提高输电线路的输送能力。
英文摘要:
      The maximum current carrying capacity of the transmission line is closely related to the meteorological parameters around the transmission line. In order to implement the decomposition and feature extraction of the historical meteorological data of the studied area accoer to solve the problem of meteorological parameter prediction, the empirical meteorological decomposition (EMD) method is used trding to the time series, and the prediction is performed in the framework of the kernel limit learning machine of the particle swarm optimization methods (PSO-KELM). The prediction results of the meteorological parameters are taken as the source data of the maximum current carrying capacity probability model of the transmission line, the computation method of the maximum current carrying capacity of the transmission line based on the probability modeling of the current density function and the meteorological parameters is proposed. The results of application analysis of a regional power grid show that the current capacity of the transmission line can be dynamically adjusted according to the prediction results of the meteorological parameters during the peak period of electricity consumption, and the transmission capacity of the transmission line can be improved under the premise of ensuring the safety and reliability of the transmission line.
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