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Un método de clasificación de producción de gas para materiales de aislamiento de cables basado en redes neuronales

Zihao Wang1, Yinan Chai1, Jingwen Gong1

  • 1School of Electrical Engineering, Sichuan University, Wuhou District, Chengdu 610207, China.

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Un nuevo modelo de aprendizaje profundo identifica con precisión múltiples patrones de falla en el aislamiento de cables de alimentación utilizando análisis de gas evolucionado. Este método avanzado mejora la precisión del diagnóstico para equipos eléctricos críticos.

Palabras clave:
aprendizaje profundomateriales de aislamiento eléctricoidentificación de tipo de fallared neuronalcables de alimentación

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Área de la Ciencia:

  • Ingeniería Eléctrica
  • Ciencia de Materiales
  • Inteligencia Artificial

Sus antecedentes:

  • El análisis de gas evolucionado (EGA) es crucial para evaluar la salud del aislamiento de los cables de alimentación de forma no invasiva.
  • Los métodos actuales tienen dificultades con los mecanismos de envejecimiento simultáneos y el reconocimiento de múltiples patrones de falla en datos de gas mixtos.

Objetivo del estudio:

  • Desarrollar un método analítico inteligente para la evaluación precisa del estado del aislamiento.
  • Proponer un marco de clasificación multietiqueta basado en redes neuronales convolucionales profundas (DCNN).

Principales métodos:

  • Se utilizaron datos de concentración de seis gases característicos de cinco materiales de aislamiento (EPDM, EVA, SR, PA, XLPE).
  • Se aplicaron técnicas de análisis de datos (transformación logarítmica, normalización Z-score) y DCNN con convolución multiescala, conexiones residuales y mecanismos de atención.
  • Se empleó una pérdida de entropía cruzada binaria ponderada para la clasificación multietiqueta de los estados de degradación.

Principales resultados:

  • El modelo DCNN aprendió eficazmente patrones de generación de gas específicos del material.
  • Identificó con precisión patrones de falla concurrentes complejos y múltiples estados de degradación simultáneamente.
  • Demostró un rendimiento superior en el reconocimiento de escenarios de falla concurrentes.

Conclusiones:

  • El marco DCNN propuesto mejora la precisión y la integralidad de la evaluación del estado del aislamiento de los cables de alimentación.
  • Proporciona un método inteligente robusto para diagnosticar condiciones de falla complejas en equipos eléctricos críticos.
  • Ofrece soporte técnico para mejorar la confiabilidad de la infraestructura de cables de alimentación.