计及不确定性的合环电流区间预测方法  

Interval Prediction Method for Loop-closing Current Considering Uncertainties

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作  者:李黄强 赵化达 舒征宇 陈林 童华敏 李欣[2] LI Huangqaing;ZHAO Huada;SHU Zhengyu;CHEN Lin;TONG Huamin;LI Xin(State Grid Yichang Power Supply Company,Yichang 443000,China;College of Electrical Engineering and New Energy,China Three Gorges University,Yichang 443000,China)

机构地区:[1]国网湖北省电力有限公司宜昌供电公司,湖北宜昌443000 [2]三峡大学电气与新能源学院,湖北宜昌443002

出  处:《智慧电力》2025年第4期88-95,共8页Smart Power

基  金:国家自然科学基金资助项目(52107107)。

摘  要:针对合环电流计算精度低且具有高随机性的问题,提出一种计及不确定性的合环电流区间预测方法。首先,为克服在合环电流预测中影响因素众多致使数据复杂的问题,提出基于自适应LASSO回归的变量选择方法,对影响因素进行选择,构建可以有效进行合环电流预测的多元数据集;其次,基于时间卷积神经网络和图卷积神经网络搭建特征提取模块充分挖掘多元数据的全局特征信息,再利用双向门控循环单元神经网络捕捉多元数据之间的长期依赖关系,进行合环电流时段预测,并对合环电流时段预测结果误差进行概率密度估计,叠加合环电流时段预测结果得到最终合环电流区间预测结果。最后,通过算例仿真验证了所提方法的优越性和可行性。To address the issues of low computational accuracy and high randomness in loop-closing current calculations,this study proposes an interval prediction method for loop-closing current that incorporates uncertainty.First,to overcome the complexity of data caused by numerous influencing factors in loop-closing current prediction,an adaptive LASSO regression-based variable selection method is introduced to identify key factors and construct a multivariate dataset capable of effective loop-closing current prediction.Second,a feature extraction module is developed using temporal convolutional networks and graph convolutional networks to fully exploit global feature information from the multivariate data.Subsequently,a bidirectional gated recurrent unit neural network is employed to capture long-term dependencies among multivariate data for the time-period prediction of loop-closing current.The prediction errors are subjected to probability density estimation,and the final interval prediction results are obtained by superimposing the time-interval predictions.Finally,case study simulations validate the superiority and feasibility of the proposed method.

关 键 词:自适应LASSO 核密度估计 特征提取 合环电流 不确定性 

分 类 号:TM715[电气工程—电力系统及自动化]

 

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