电力系统保护与控制2024，Vol.52Issue(10)：11-20,10.DOI:10.19783/j.cnki.pspc.231503
基于深度学习融合网络的含噪电能质量扰动识别方法
Identification of power quality disturbance with noises based on an integrated deep learning network
摘要
Abstract
A novel method combined with adaptive wavelet threshold noise reduction and deep learning is proposed to improve the accuracy of identifying power quality disturbances in strong-noise environments.First,the noise-containing disturbance signals are noise-reduced by a threshold function algorithm based on an improved peak and score level adaptive thresholding and energy optimization.Then,the residual network is used to extract deep features from the noise-reduced disturbance signals,based on which the bidirectional long short term memory network under the multi-attention mechanism is incorporated to establish temporal feature dependency.This constitutes a framework applicable to the recognition of disturbance signals in a noisy environment.Finally,numerical simulations are carried out on 20 types of disturbance signals in different noise environments.It can be seen from the results that the proposed method has good noise robustness and high recognition accuracy in different noise environments.关键词
电能质量扰动/自适应小波降噪/残差神经网络/多头注意力/双向长短时记忆网络Key words
power quality disturbances/adaptive wavelet threshold/residual neural network/multi-headed attention/bidirectional long-short term memory network引用本文复制引用
王海东,程杉,徐其平,刘烨,王灿..基于深度学习融合网络的含噪电能质量扰动识别方法[J].电力系统保护与控制,2024,52(10):11-20,10.基金项目
This work is supported by the National Natural Science Foundation of China(No.52107108). 国家自然科学基金项目资助(52107108) （No.52107108）
