电气技术2024，Vol.25Issue(5)：51-56,64,7.
基于1DCNN-BiLSTM-BiGRU的电能质量扰动分类方法
The classification method of power quality disturbance based on 1DCNN-BiLSTM-BiGRU
摘要
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.关键词
电能质量/一维卷积神经网络(1DCNN)/双向长短期记忆(BiLSTM)网络/双向门控循环单元(BiGRU)Key words
power quality/one-dimensional convolutional neural network(1DCNN)/bidirectional long short-term memory(BiLSTM)/bidirectional gated recurrent unit(BiGRU)引用本文复制引用
王立辉,柯泳,苏如开..基于1DCNN-BiLSTM-BiGRU的电能质量扰动分类方法[J].电气技术,2024,25(5):51-56,64,7.基金项目
国家自然科学基金项目(62271199) （62271199）
