Power quality disturbances recognition using adaptive chirp mode pursuit and grasshopper optimized support vector machines

被引：41
作者：
Motlagh, Shayan Z. T. [1 ]
Foroud, Asghar Akbari [1 ]
机构：
[1] Semnan Univ, Elect & Comp Engn Dept, Semnan, Iran
来源：
MEASUREMENT | 2021年 / 168卷
关键词：
Power quality disturbances recognition; adaptive chirp mode pursuit (ACMP); support vector machine (SVM); Grasshopper optimization algorithm; Machine learning; FEATURE-SELECTION; WAVELET TRANSFORM; S-TRANSFORM; TIME-FREQUENCY; CLASSIFICATION; SYSTEM; DECOMPOSITION; ALGORITHM; IDENTIFICATION; PERFORMANCE;
D O I：
10.1016/j.measurement.2020.108461
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
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 reduce 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.
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页数：20
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