Power quality disturbance classification based on efficient adaptive Arrhenius artificial bee colony feature selection,International Transactions on Electrical Energy Systems

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Power quality disturbance classification based on efficient adaptive Arrhenius artificial bee colony feature selection
International Transactions on Electrical Energy Systems ( IF 1.9 ) Pub Date : 2021-03-23 , DOI: 10.1002/2050-7038.12868
Zamrooth Dawood ₁ , Babulal C K ₂

Affiliation

Automatic power quality disturbance (PQD) classification is a challenging task for industry and utility. This work presents an algorithm for classifying the PQDs based on the combination of novel feature selection with machine learning (ML) techniques. In this work, discrete wavelet transform (DWT) is utilized to decompose the PQD signals and hence minimize the size of input vectors with multi‐resolution analysis. Novel optimal feature selection‐based classification is performed using probabilistic neural network and adaptive Arrhenius artificial bee colony (PNN‐ adaptive AABC) algorithm. The selection of optimal features may discard the redundant features and retain the useful features. Also, this adaptive AABC algorithm provides a higher convergence rate, and this is used for accurate feature selection since this work is applied for 16 PQD events. In other case, features extracted with DWT are processed and trained with various ML algorithms such as Decision Tree, support vector machine (SVM), and K‐nearest neighbor (KNN). Finally, the comparison accuracy of the proposed classifier is compared with the existing classifiers. The proposed technique found to give improved results in most of the cases like accuracy, prediction speed, and training time, and the outcomes acquired as 99.98%, ~1260 obs/seconds, and 13.113 seconds, respectively.

中文翻译：

基于有效自适应阿伦尼乌斯人工蜂群特征选择的电能质量扰动分类

自动电能质量扰动（PQD）分类对工业和公用事业而言是一项艰巨的任务。这项工作提出了一种基于新颖特征选择与机器学习（ML）技术相结合的PQD分类算法。在这项工作中，离散小波变换（DWT）用于分解PQD信号，从而通过多分辨率分析将输入矢量的大小最小化。使用概率神经网络和自适应Arrhenius人工蜂群（PNN-自适应AABC）算法执行基于最佳特征选择的新型分类。最佳功能的选择可能会丢弃多余的功能，并保留有用的功能。而且，这种自适应AABC算法可提供更高的收敛速度，由于这项工作适用于16个PQD事件，因此可用于准确的功能选择。在其他情况下，用DWT提取的特征将通过各种ML算法进行处理和训练，例如决策树，支持向量机（SVM）和K最近邻（KNN）。最后，将提出的分类器的比较精度与现有分类器进行比较。所提出的技术发现在大多数情况下（例如准确性，预测速度和训练时间）可以提供更好的结果，并且获得的结果分别为99.98％，〜1260 obs / s和13.113秒。将拟议分类器的比较精度与现有分类器进行比较。所提出的技术发现在大多数情况下（例如准确性，预测速度和训练时间）可以提供更好的结果，并且获得的结果分别为99.98％，〜1260 obs / s和13.113秒。将拟议分类器的比较精度与现有分类器进行比较。所提出的技术发现在大多数情况下（例如准确性，预测速度和训练时间）可以提供更好的结果，并且获得的结果分别为99.98％，〜1260 obs / s和13.113秒。

更新日期：2021-05-03

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