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

H. M. Cortã‰s Campos, J. F. Gã“mez-Aguilar, C. J. ZÚÑIGA-AGUILAR, L. F. Avalos-Ruiz and J. E. Lavã N-Delgado
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H. M. Cortã‰s Campos: Centro Nacional de Investigación y Desarrollo Tecnológico (CENIDET), Tecnológico Nacional de México, Interior Internado Palmira S/N, Col. Palmira, C. P. 62490, Cuernavaca, MR, Mexico
J. F. Gã“mez-Aguilar: ��Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCyT), Centro Nacional de Investigación y Desarrollo Tecnológico (CENIDET), Tecnológico Nacional de México, Interior Internado Palmira S/N, Col. Palmira, C. P. 62490, Cuernavaca, MR, Mexico
C. J. ZÚÑIGA-AGUILAR: ��Panzura Data Services, Blvr. Puerta de Hierro 5153, Puerta de Hierro, 45116 Zapopan, JA, Mexico
L. F. Avalos-Ruiz: Centro Nacional de Investigación y Desarrollo Tecnológico (CENIDET), Tecnológico Nacional de México, Interior Internado Palmira S/N, Col. Palmira, C. P. 62490, Cuernavaca, MR, Mexico
J. E. Lavã N-Delgado: �Dirección de Ingeniería en Redes y Telecomunicaciones, Universidad Politécnica del Estado de Guerrero (UPEG), Puente Campuzano, Carretera Federal Iguala-Taxco K. M. 105, Taxco de Alarcón, C. P. 40321, GE, Mexico

FRACTALS (fractals), 2024, vol. 32, issue 03, 1-30

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.

Keywords: Fault Diagnosis Transformer; Power Quality Disturbances; Fault Diagnosis PV System; Fault Diagnosis Induction Motor; Fractional Fourier Transform; Neural Networks; Wavelet Transform (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X24500592

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