计及灰数据的知识-数据驱动低压有源配电网潮流计算

刘斯亮; 郑泽南; 张勇军; 羿应棋; 池玉泉

刘斯亮,郑泽南,张勇军,羿应棋,池玉泉.计及灰数据的知识-数据驱动低压有源配电网潮流计算[J].电测与仪表,2025,62(6):2-10.
LIU Siliang,ZHENG Zenan,ZHANG Yongjun,YI Yingqi,CHI Yuquan.Knowledge-data-driven power flow calculation for low-voltage active distribution network considering gray data[J].Electrical Measurement & Instrumentation,2025,62(6):2-10.

计及灰数据的知识-数据驱动低压有源配电网潮流计算

Knowledge-data-driven power flow calculation for low-voltage active distribution network considering gray data

DOI：10.19753/j.issn1001-1390.2025.06.001

中文关键词: 低压配电网潮流计算知识-数据融合多通道卷积灰数据

英文关键词:low-voltage distribution network, power flow calculation, knowledge-data fusion, multi-channel convolution, gray data

基金项目:国家自然科学基金资助项目（52177085）

作者中文名	作者英文名	单位
刘斯亮	LIU Siliang	华南理工大学电力学院,智慧能源工程技术研究中心
郑泽南	ZHENG Zenan	华南理工大学电力学院,智慧能源工程技术研究中心
张勇军	ZHANG Yongjun	华南理工大学电力学院,智慧能源工程技术研究中心
羿应棋	YI Yingqi	华南理工大学电力学院,智慧能源工程技术研究中心
池玉泉	CHI Yuquan	华南理工大学电力学院,智慧能源工程技术研究中心

摘要点击次数: 36

中文摘要:

低压配电网拓扑和线路参数不准确使得传统的潮流计算方法失效，采用数据驱动方法能减少对物理参数的依赖，但缺乏可解释性。为此，提出一种融合物理知识与数据驱动的潮流计算方法。基于DistFlow模型构造了深度学习模型的输入输出特征向量，以低压配电台区的首端节点电压、用户节点光伏出力及负荷功率作为输入特征，用户节点电压幅值作为输出特征。结合三相线性潮流模型设计多通道卷积网络，通过独立通道处理电压、有功功率和无功功率，并利用电阻、电抗参数初始化卷积核权重。最后，针对灰数据（含有量测误差和异常值的数据）用于训练会影响模型性能的问题，提出改进降噪自编码器筛选并剔除异常样本。实验表明，所提方法在准确性和泛化性能上优于传统数据驱动方法，同时显著降低了灰数据对模型的影响。

英文摘要:

Inaccurate topology and line parameters in low-voltage distribution networks(LVDNs) render traditional power flow calculation methods ineffective. While data-driven approaches reduce reliance on physical parameters, they often lack interpretability. To address this, we propose a hybrid method integrating physical knowledge with data-driven techniques. The input-output feature vectors of the deep learning model are constructed based on the DistFlow model, where the head-end node voltage, photovoltaic(PV) output, and load power at user nodes serve as input features, and the voltage magnitude at user nodes is the output feature. A multi-channel convolutional network is designed by incorporating a three-phase linearized power flow model. This network processes voltage, active power, and reactive power through independent channels, with convolutional kernel weights initialized using resistance(R) and reactance(X) parameters. Finally, aiming at the problem that gray data(data containing measurement errors and outliers) used for training will affect model performance, an improved denoising autoencoder(DAE) is proposed to filter and eliminate anomalous samples. Experimental results demonstrate that the proposed method outperforms conventional data-driven approaches in accuracy and generalization capability, while significantly reducing the influence of gray data on model performance.

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