Abstract：The accurate identification of hidden danger for power utilization in low-voltage substations plays an

important role in improving the quality of power supply and reducing the risk of accidents.To improve the accuracy

of identifying hidden danger in low-voltage substations，a low-voltage user hidden danger for power utilization

identification model based on SSAE-SSA-GRU was proposed. Firstly，the user's original voltage data was

normalized，and the feature parameters of the data were extracted through a stacked spares auto-encoder（SSAE）to solve the redundancy problem caused by the high dimensionality of the original voltage data. Then，the sparrow

search algorithm（SSA）was introduced to optimize the hyperparameters of the gated recurrent unit（GRU）

network，improving the accuracy of the model's fault diagnosis results.Finally，the performance of the established

SSAE-SSA-GRU model was evaluated through numerical examples，verifying the effectiveness of the proposed

method in identifying hidden danger for power utilization for low-voltage users. Compared with traditional methods

for identifying abnormal electricity usage，the proposed method has good convergence and high accuracy.

      Key words：low-voltage substation users；identification of hidden danger for power utilization；stacked spares

auto-encoder（SSAE）；sparrow search algorithm（SSA）；gated recurrentl unit（GRU）