基于1DCNN-BiLSTM-BiGRU的电能质量扰动分类方法

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Abstract To address the issue of reduced recognition accuracy in identifying power quality disturbance (PQD) due to noise interference, this paper introduces a PQD classification method based on one-dimensional convolutional neural network (1DCNN)-bidirectional long short-term memory (BiLSTM)-bidirectional gated recurrent unit (BiGRU). This method initially utilizes 1DCNN to effectively extract shallow local features from the raw signals. Subsequently, it employs a combination of BiLSTM and BiGRU modules to delve deeper into temporal information and contextual relationships, facilitating the extraction of deep temporal features. Finally, the extracted features are input to the classification module for PQD recognition. Simulation results show that the proposed method has better accuracy and stronger noise resistance.

Key words： power quality one-dimensional convolutional neural network (1DCNN) bidirectional long short-term memory (BiLSTM) bidirectional gated recurrent unit (BiGRU)

Received: 03 January 2024

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	Articles by authors
	WANG Lihui
	KE Yong
	SU Rukai

Cite this article:

WANG Lihui,KE Yong,SU Rukai. The classification method of power quality disturbance based on 1DCNN-BiLSTM-BiGRU[J]. Electrical Engineering, 2024, 25(5): 51-56.

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http://dqjs.cesmedia.cn/EN/Y2024/V25/I5/51

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