电气技术2024，Vol.25Issue(9)：9-13,21,6.
基于变分模态分解-门控循环单元-麻雀搜索算法的电能质量稳态指标预测
Power quality steady-state index prediction based on variational mode decomposition-gated recurrent unit-sparrow search algorithm
摘要
Abstract
The accurate prediction of power quality helps to ensure the safe and reliable operation of the power grid. This article proposes a hybrid model based on variational mode decomposition (VMD),gated recurrent unit (GRU),and sparrow search algorithm (SSA) for predicting steady-state index of power quality. Firstly,the VMD is used to decompose historical power quality data. Then the parameters of GRU neural network is optimize based on SSA,and the decomposed power quality components are input into the GRU neural network. Finally,the predicted values of each component are added together to obtain the predicted steady-state index of power quality. The model is validated using power quality data from a monitoring point,and compared with GRU and VMD-GRU models. The results show that the proposed prediction model has a mean absolute percentage error of less than 7％,indicating better prediction performance.关键词
电能质量/变分模态分解(VMD)/麻雀搜索算法(SSA)/门控循环单元(GRU)Key words
power quality/variational mode decomposition (VMD)/sparrow search algorithm (SSA)/gated recurrent unit (GRU)引用本文复制引用
黄华鸿..基于变分模态分解-门控循环单元-麻雀搜索算法的电能质量稳态指标预测[J].电气技术,2024,25(9):9-13,21,6.基金项目
国家自然科学基金(51404103,51574117,61376073) （51404103,51574117,61376073）
