APPLICATION OF FRACTIONAL-ORDER INTEGRAL TRANSFORMS IN THE DIAGNOSIS OF ELECTRICAL SYSTEM CONDITIONS

Fractals Pub Date : 2024-03-26 DOI:10.1142/s0218348x24500592
H. M. CORTÉS CAMPOS, J. F. GÓMEZ-AGUILAR, C. J. ZÚÑIGA-AGUILAR, L. F. AVALOS-RUIZ, J. E. LAVÍN-DELGADO
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Abstract

This paper proposes a methodology for the diagnosis of electrical system conditions using fractional-order integral transforms for feature extraction. This work proposes three feature extraction algorithms using the Fractional Fourier Transform (FRFT), the Fourier Transform combined with the Mittag-Leffler function, and the Wavelet Transform (WT). Each algorithm extracts data from an electrical system to obtain a set of features that are classified by an Artificial Neural Network to determine the system’s condition. The algorithms are utilized in diagnosing two types of electrical machine faults, one in a photovoltaic system, and another in classifying the power quality disturbances (PQDs). An optimization algorithm is suggested to find the optimal orders of integral transforms. The obtained results demonstrate the system’s effective diagnosis, displaying superior performance in classifying the faults and PQDs with complex signals.

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分数阶积分变换在电气系统状况诊断中的应用
本文提出了一种利用分数阶积分变换进行特征提取的电气系统状况诊断方法。这项工作提出了三种特征提取算法,分别使用分数傅里叶变换 (FRFT)、结合 Mittag-Leffler 函数的傅里叶变换和小波变换 (WT)。每种算法都从电气系统中提取数据,获得一组特征,通过人工神经网络对这些特征进行分类,从而确定系统的状况。这些算法被用于诊断两种类型的电机故障,一种是光伏系统故障,另一种是电能质量干扰(PQD)分类。还提出了一种优化算法来寻找积分变换的最佳阶数。结果表明,该系统能有效地进行诊断,在对故障和具有复杂信号的电能质量干扰进行分类方面表现出卓越的性能。
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