Power Quality Disturbance Identification Method Based on VMD-SAST
张博智 1张茹 1焦东翔 1王龙宇 1周一凡 1周丽霞1
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作者信息
1. 国网冀北电力有限公司计量中心,北京 100032
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摘要
新能源大规模并网以及电力电子设备广泛应用引起的复杂电能质量扰动(power quality disturbances,PQDs)会威胁电力系统的安全稳定运行.针对复杂PQDs难以精准检测识别的问题,提出了一种基于变分模态分解(variational mode decomposition,VMD)与同步压缩自适应 S 变换(synchrosqueezing adaptive S-transform,SAST)的PQDs检测识别方法.首先,使用VMD将PQDs信号分解为多个模态分量,每个分量只保留局部扰动特征,降低PQDs信号的复杂度;其次,提取一种SAST时频分析方法,改善时频分辨率,集中频谱中的能量分布,提高对PQDs信号的检测精度;最后,基于VMD-SAST提取扰动特征,利用3种不同算法实现对PQDs信号的分类识别.通过仿真分析表明:所提出的方法具有较高的PQDs分类识别精度、较高的适用性和较强的抗噪声能力.
Abstract
Complex power quality disturbances(PQDs)caused by large-scale grid-connection of renewable energy and wide application of power electronic equipment will threaten the safe and stable operation of power system.Aiming at the difficulty of accurate detection and recognition of complex PQDs,a PQDs detection and recognition method based on variational mode decomposition(VMD)and synchrosqueezing adaptive S-transform(SAST)is proposed.Firstly,the VMD is used to PQDs signals into multiple modal components,with each component only preserving local disturbance features so as to reduce the complexity of PQDs signals.Secondly,a SAST time-frequency analysis method is proposed to improve the time-frequency resolution,concentrate the energy distribution in the spectrum and improve the detection accuracy of PQDs signals.Finally,the disturbance features are extracted based on VMD-SAST,and the PQDs signals are classified and recognized by 3 algorithms respectively.The simulation results show that the proposed method has high PQDs classification and recognition accuracy,high applicability and strong anti-noise ability.
关键词
电能质量扰动/变分模态分解/特征提取/机器学习
Key words
power quality disturbances/variational mode decomposition/feature extraction/machine learning
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