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1.四川大学电气工程学院,四川省成都市 610065
2.安徽大学电气工程与自动化学院,安徽省合肥市 230000
3.国家电能变换与控制工程技术研究中心(湖南大学),湖南省长沙市 410082
Received:20 November 2023,
Revised:2024-04-26,
Published Online:02 August 2024,
Published:10 December 2024
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汪颖,李龙,陈韵竹等.基于核心关联指标和虚拟等值电阻的台区理论线损计算[J].电力系统自动化,2024,48(23):121-135.
WANG Ying,LI Long,CHEN Yunzhu,et al.Theoretical Calculation of Line Loss in Distribution Station Areas Based on Core Correlation Indicators and Virtual Equivalent Resistance[J].Automation of Electric Power Systems,2024,48(23):121-135.
汪颖,李龙,陈韵竹等.基于核心关联指标和虚拟等值电阻的台区理论线损计算[J].电力系统自动化,2024,48(23):121-135. DOI: 10.7500/AEPS20231120006.
WANG Ying,LI Long,CHEN Yunzhu,et al.Theoretical Calculation of Line Loss in Distribution Station Areas Based on Core Correlation Indicators and Virtual Equivalent Resistance[J].Automation of Electric Power Systems,2024,48(23):121-135. DOI: 10.7500/AEPS20231120006.
为提升传统线损计算方法的适用性和准确性,解决基于神经网络的线损计算方法的可解释性问题,提出了基于核心关联指标和虚拟等值电阻的台区理论线损计算模型。首先,提出了线损核心关联指标筛选方法,引用灰色关联度算法,计算线损关联指标与线损的相关性,筛选出核心线损关联指标,提高了模型的可解释性。其次,提出了物理-数据双驱动的理论线损计算模型,该模型考虑核心关联指标,以虚拟等值电阻替代传统等值电阻实现线损计算,提升了模型的适用性和准确性。然后,提出了基于二阶锥算法的参数估计方法,构建以理论线损改进值与实际值的方差最小的目标函数,以各类关联指标的临界值为约束条件,求解模型的参数。最后,应用实际监测数据和仿真模型数据,验证了文中所提方法的有效性和适用性。
In order to improve the applicability and accuracy of traditional line loss calculation methods
and solve the interpretability problem of neural network based line loss calculation methods
the theoretical calculation model of line loss in distribution station areas based on core correlation indicators and virtual equivalent resistance is proposed. Firstly
a screening method for core correlation indicators of line loss is proposed. The grey relation algorithm is used to calculate the correlation between line loss correlation indicators and line loss
and to screen out core correlation indicators
thereby improving the interpretability of the model. Secondly
a physics-data driven theoretical calculation model of line loss is proposed. The model considers core correlation indicators and replaces traditional equivalent resistance with virtual equivalent resistance to achieve line loss calculation
thus improving the applicability and accuracy of the model. Then
a parameter estimation method based on the second-order cone algorithm is proposed
and an objective function is constructed to minimize the residual equation between the improvement value of theoretical line loss and the actual value. The critical values of various related indicators are used as constraints to solve the model parameters. Finally
the effectiveness and applicability of the proposed method are verified by applying actual monitoring data and simulation model data.
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