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湖南大学电气与信息工程学院,湖南省长沙市 470082
申亚涛(1991—),男,博士研究生,主要研究方向:电力系统优化调度。E-mail:hnusyt@hnu.edu.cn
谷紫文(1995—),男,博士研究生,主要研究方向:电力大数据分析与建模、智能信息处理。E-mail:ziwengu@hnu.edu.cn
李文美(2000—),女,硕士研究生,主要研究方向:电力系统优化调度。E-mail:liwenmei@hnu.edu.cn
黄纯(1966—),男,通信作者,博士,教授,博士生导师,主要研究方向:配电网保护与控制、电网优化运行等。E-mail:yellowpure@hotmail.com
收稿:2023-12-06,
修回:2024-08-12,
网络出版:2024-11-06,
纸质出版:2024-12-25
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申亚涛,谷紫文,李文美等.基于电压和功率数据的有源低压台区用户相别辨识方法[J].电力系统自动化,2024,48(24):145-154.
SHEN Yatao,GU Ziwen,LI Wenmei,et al.Phase Identification Method for Customers in Active Low-voltage Transformer Area Based on Voltage and Power Data[J].Automation of Electric Power Systems,2024,48(24):145-154.
申亚涛,谷紫文,李文美等.基于电压和功率数据的有源低压台区用户相别辨识方法[J].电力系统自动化,2024,48(24):145-154. DOI: 10.7500/AEPS20231206001.
SHEN Yatao,GU Ziwen,LI Wenmei,et al.Phase Identification Method for Customers in Active Low-voltage Transformer Area Based on Voltage and Power Data[J].Automation of Electric Power Systems,2024,48(24):145-154. DOI: 10.7500/AEPS20231206001.
低压台区中存在用户实际相别与标定值不一致的现象,对台区的主动调控与精细化管理构成了障碍。当前的相别辨识方法在面临高比例分布式光伏接入、拓扑结构信息不完整及线路参数缺失的有源低压台区时,具有一定的局限性。为此,文中提出了一种基于电压与功率采集数据的有源低压台区用户相别辨识方法。首先,通过分析低压台区三相线性化DistFlow潮流模型,得到相别辨识的理论依据。然后,通过松弛0-1变量构建多元线性回归模型,并利用递推最小二乘法求解,实现相别辨识,避免了矩阵求逆。此外,还引入了相别隶属度指标强化计算结果。光伏台区试点应用结果显示,该方法的识别精度高,鲁棒性好,不易受电压平衡程度、节点数量和三相分布式光伏等因素的影响。
In low-voltage transformer areas
the phenomenon that the actual phase of customers is inconsistent with the phase setting value exists
which poses an obstacle to the active regulation and refined management of the transformer area. The current phase identification methods have certain limitations when facing the active low-voltage transformer areas with a high proportion of distributed photovoltaic access
incomplete topological information and missing line parameters. Therefore
this paper proposes a phase identification method for customers in active low-voltage transformer areas based on the collected data of voltage and power. Firstly
by analyzing the three-phase linearized DistFlow power flow model of the low-voltage transformer area
the theoretical basis for phase identification is obtained. Then
a multiple linear regression model is constructed by relaxing the 0-1 variables
and the recursive least square method is used for solution to realize the phase identification and avoid the matrix inversion. In addition
the phase membership index is introduced to strengthen the calculation results. The pilot application results in the photovoltaic transformer area show that this method has high identification accuracy and good robustness
and is not easily affected by the factors such as the degree of voltage balance
the number of nodes and three-phase distributed photovoltaic
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