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.