With the proposal of the national dual-carbon strategic goal

distributed photovoltaic (PV) ushers in new development opportunities

and the management of voltage quality for photovoltaic power stations becomes a key challenge. Based on the dual flow long short-term memory-convolutional neural network (LSTM-CNN) model

this paper proposes an interaction layer and a multi-feature fusion prediction module to achieve the prediction of photovoltaic user voltage quality indicators. The operating status feedback data of different periods and the same interval is monitored

and the data is filtered by exponential moving average to reduce the influence of noise. The dual-stream LSTM-CNN model for sequence modeling is constructed

with the introduction of attention mechanism aimed at enhancing attention to key features. We propose a multi-feature fusion module

which fully leverages digital information from different levels to enrich the feature representation for prediction. This module consists of six prediction heads

allowing us to predict future voltage fluctuations

voltage variations

and voltage deviations (voltage quality indices). Model performance is evaluated using the mean absolute error (MAE) and the root mean square error (RMSE)

where the simulation of voltage deviation MAE is 0. 029 0

showing a small prediction error. The experimental results demonstrate that the proposed method can effectively predict the voltage quality of low voltage distributed PV

and provide important support and reference for the stable operation of low voltage distributed PV platform area.