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.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |