摘要
针对复杂噪声环境下电能质量扰动(power quality disturbance,PQD)识别精度低的问题,将软阈值函数与一维卷积神经网络相结合,提出了一种用于电能质量扰动识别的深度卷积去噪网络(deep convolutional denoising network,DCDN)。将软阈值函数作为非线性转换层插入到深层网络中,构造软阈值去噪模块以有效地消除噪声及其他冗余特征。软阈值去噪模块作为网络的可学习参数,可以通过模型训练确定其权重。相比于软阈值函数,所提去噪模块能够针对不同样本输入自适应地计算其阈值。仿真结果表明,所提方法在不同噪声水平下,对18种常见的单一和复合电能质量扰动均能有效识别。相比于其他常见的扰动识别算法,所提方法抗噪性强,复杂噪声环境下电能质量扰动的识别精度高。
Aiming at the problem of low recognition accuracy of power quality disturbance(PQD) in complex noise environment, this paper combines the soft threshold function with one-dimensional convolutional neural network, and proposes a deep convolutional denoising network for power quality disturbance recognition. The soft-threshold function is inserted into the deep network as a nonlinear transformation layer, and a soft-threshold denoising module is constructed to effectively remove noise or other redundant features. Soft-threshold denoising networks are learnable parameters whose weights can be determined through model training.Compared with the soft threshold function, the proposed denoising module can adaptively calculate its thresholds from different sample inputs. The simulation results show that the proposed method can effectively identify 18 common single and multiple PQD in the case of different noise environment. Compared with other common disturbance identification algorithms, the proposed method has better anti-noise and high recognition accuracy of PQD in complex noise environment.
作者
奚鑫泽
邢超
覃日升
郭成
周鑫
XI Xinze;XING Chao;QIN Risheng;GUO Cheng;ZHOU Xin(Electric Power Research Institute of Yunnan Power Grid Co.,Ltd.,Kunming 650217,China)
出处
《南方电网技术》
CSCD
北大核心
2022年第12期118-125,共8页
Southern Power System Technology
基金
国家重点研发计划(2019YFE0118000)
国家自然科学基金资助项目(52167011)。
关键词
电能质量
软阈值去噪
卷积神经网络
深度学习
power quality
soft-threshold denoising
convolutional neural network
deep learning
