基于多融合MVMD-ISVM的复杂电能质量扰动识别方法

Identification method for complex power quality disturbances based on multi-fusion MVMD-ISVM

可再生能源大规模部署趋势之下,针对逆变器等高频电力电子设备所引发的日趋严峻的暂态电能质量问题,提出一种基于多融合改进变分模态分解(modified variational mode decomposition,MVMD)与优化支持向量机(improved support vector machine,ISVM)的复杂电能质量扰动识别方法,进而促进电能质量提升。首先,MVMD算法利用模态总能量占比最大的原则自适应调节扰动模态分解层数,从而构造最优扰动模态特征向量。然后,通过构造多融合组合核函数结构实现原始扰动信号与扰动模态特征向量的有效映射与融合,形成ISVM模型,提升扰动识别准确率。最后,通过多个测试对比,表明所提的多融合MVMD-ISVM模型可有效地对扰动信号进行特征提取与识别分类,且具有更高的识别精度与抗噪性能,适用于现代电网中复杂电能质量扰动信号的快速、准确识别。

Under the trend of large-scale deployment of renewable energy,aiming at the increasingly severe transient power quality problems caused by high-frequency power electronic equipment such as inverters,a complex power quality disturbance identification method based on modified multi-fusion variational mode decomposition and improved support vector machine is proposed to improve power quality.Firstly,the improved variational mode decomposition algorithm uses the principle of the maximum total energy ratio of the mode to adaptively adjust the number of disturbance mode decomposition layers,thereby constructing the optimal disturbance mode eigenvector.Then,by constructing a multi-fusion combined kernel function structure,the effective mapping and fusion of the original disturbance signal and the disturbance mode feature vector is realized,and an optimized support vector machine model is formed to improve the accuracy of disturbance recognition.Finally,the results of multiple comparative experiments show that the proposed Multi-fusion MVMD-ISVM model can achieve the effective feature extraction and identification of disturbance signals,and has a higher recognition effect and anti-noise performance,which is suitable for accurate and fast recognition of complex power quality disturbance signals in modern power grids.