陈晓华,王志平,吴杰康,蔡锦健,张勋祥,阚东旺,陈敦进
CHEN Xiaohua,WANG Zhiping,WU Jiekang,CAI Jinjian,ZHANG Xunxiang,KAN Dongwang,CHEN Dunjin

• 1:东莞理工学院电信工程与智能化学院
• 2:广东工业大学自动化学院

摘要(Abstract)：

针对SVM（支持向量机）的惩罚因子和核函数参数在寻优过程中容易陷入局部最优以及哈里斯鹰优化算法容易陷入局部最优的问题，提出一种利用IHHO（改进哈里斯鹰优化）算法对SVM的惩罚因子和核函数参数进行寻优，构建IHHO-SVM分类器来对电能质量扰动信号进行识别的方法。通过对9种不同的电能质量扰动信号加入0 dB、20 dB和30 dB的高斯白噪声来进行测试，利用改进的自适应噪声完备集合经验模式分解算法分解信号并提取信号前3阶固有模态函数分量的能量熵和样本熵作为一组特征向量，将特征向量进行归一化处理后输入9种分类器进行对比。仿真结果表明，在信号加入0 dB、20 dB和30 dB高斯白噪声的情况下，IHHO-SVM分类器的识别准确率分别为99.11%、97.78%和97.33%，其分类效果是所有分类器中最优的，证明了其分类的准确性、优越性和抗噪性。
Aiming at the problems that the penalty factor and kernel function parameters of SVM(support vector machine) are easy to fall into the local optimal solution in the optimization process and the Harris Hawks optimization algorithm is easy to fall into the local optimal solution, a method of using IHHO(improved Harris Hawks optimization) algorithm to optimize the penalty factor and kernel function parameters of SVM and constructing IHHO-SVM classifier to identify disturbance signals of power quality is proposed. By adding 0 dB,20 dB and 30 dB Gaussian white noises to nine different disturbance signals of power quality, the improved empirical mode decomposition algorithm of adaptive noise complete set is used to decompose the signal, and the energy entropy and sample entropy of the first three intrinsic mode function components of the signal are extracted as a set of feature vectors. The feature vectors are normalized and input into nine classifiers for comparison. The simulation results show that the recognition accuracy of IHHO-SVM classifier is 99.11%, 97.78% and 97.33%, respectively, when the signal is added with 0 dB,20 dB and 30 dB Gaussian white noises. The classification effect of IHHO-SVM classifier is the best among all classifiers, which proves the accuracy, superiority and noise immunity of its classification.

关键词(KeyWords)： 电能质量;扰动信号;支持向量机;哈里斯鹰优化算法;扰动识别
power quality;disturbance signal;support vector machine;Harris Hawks optimization algorithm;disturbance identification

Abstract:

Keywords:

基金项目(Foundation): 广东省基础与应用基础研究基金项目（2019B1515120076）;广东省基础与应用基础研究基金区域联合基金项目“粤港澳研究团队项目”（2020B1515130001）

作者(Author): 陈晓华,王志平,吴杰康,蔡锦健,张勋祥,阚东旺,陈敦进
CHEN Xiaohua,WANG Zhiping,WU Jiekang,CAI Jinjian,ZHANG Xunxiang,KAN Dongwang,CHEN Dunjin

DOI: 10.19585/j.zjdl.202308015

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