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

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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.

Key words： power quality variational mode decomposition (VMD) sparrow search algorithm (SSA) gated recurrent unit (GRU)

Received: 18 March 2024

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	HUANG Huahong

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HUANG Huahong. Power quality steady-state index prediction based on variational mode decomposition-gated recurrent unit-sparrow search algorithm[J]. Electrical Engineering, 2024, 25(9): 9-13.

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http://dqjs.cesmedia.cn/EN/Y2024/V25/I9/9

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