This paper presents a new method for identifying different types of power quality disturbances (PQDs). In this article, adaptive chirp mode pursuit (ACMP) is used to extract useful features and the greedy algorithm that uses the similar principles of the matching pursuit method is adopted in the ACMP. Besides, the ACMP takes advantage of the sparse matrices, which reduces the computational cost. The infinite feature selection as the filter-based algorithm is applied for the elimination of improper features. Also, the grasshopper optimization algorithm (GOA) is used to optimize the parameters of the SVM as the classifier. The obtained results of the simulations and real disturbances show the high accuracy and speed of the proposed algorithm, which makes it possible to use it in power quality measuring and analysis devices. Also, the proposed method has noise rejection capability, which is another advantage that justifies its use in measuring and analyzing PQDs.

本文提出了一种用于识别不同类型的电能质量扰动（PQD）的新方法。在本文中，使用自适应线性调频模式追踪（ACMP）提取有用的特征，并且在ACMP中采用了采用匹配追踪方法相似原理的贪婪算法。此外，ACMP利用稀疏矩阵的优势，从而降低了计算成本。无限特征选择作为基于过滤器的算法被应用于消除不适当的特征。此外，蚱optimization优化算法（GOA）用于优化SVM作为分类器的参数。仿真结果和实际扰动结果表明，该算法具有较高的准确性和速度，可以用于电能质量测量和分析设备中。也，