电气传动2025，Vol.55Issue(7)：78-86,9.DOI:10.19457/j.1001-2095.dqcd26165
基于SSAE-SSA-GRU的低压用户用电隐患识别方法研究
Study on Identification Method of Hidden Danger for Power Utilization of Low-voltage Users Based on SSAE-SSA-GRU
摘要
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.关键词
低压台区用户/用电隐患识别/堆栈稀疏自编码器/麻雀搜索算法/门控循环单元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)分类
信息技术与安全科学引用本文复制引用
庞博,蒙静,张洋,塔娜,王海波,杜晶..基于SSAE-SSA-GRU的低压用户用电隐患识别方法研究[J].电气传动,2025,55(7):78-86,9.基金项目
内蒙古电力集团(有限)责任公司科技项目(低压配电线路信道监测与智能诊断技术深化应用研究) （有限）
