基于深度网络的新型电力系统谐波分离算法研究
Research on Harmonic Separation Algorithm for New Power Systems Based on Deep Networks
姚兵 1申冉2
作者信息
- 1. 国网湖北省电力有限公司直流公司,湖北宜昌 443000
- 2. 国网湖北省电力有限公司宜昌供电公司,湖北宜昌 443099
- 折叠
摘要
新型电力系统中广泛应用电力电子装置,导致谐波成分复杂且随时间变化.有效检测这些谐波分量对于提升电能质量与效率具有重要意义.集合经验模态分解能够求解电力系统信号的频谱信息,并提取各次谐波的幅值、相位等特征但需人为设定参数.提出了一种基于深度网络的神经网络训练自适应谐波分解模型,该模型能够自动选择参数,无须依赖人工设置,从而避免了人为因素引入的误差.最后,验证了所提出方法的有效性.
Abstract
The new power systems contain a large number of power electronic devices,resulting in complex har-monic content,with harmonic frequencies varying over time.Monitoring the harmonic components can improve the power quality and efficiency of these systems.Empirical Mode Decomposition(EMD)can resolve the spectral information of power system signals,such as the amplitude and phase of each harmonic.This paper proposes an adaptive harmonic decomposition model based on deep neural networks,where the model parameters can be auto-matically selected without relying on manual settings,thus avoiding human-induced errors.Finally,test cases are presented to verify the effectiveness of the proposed method.
关键词
深度网络/神经网络/新型电力系统/谐波分离Key words
deep network/neural network/new power system/harmonic separation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据