Inspección de subestaciones eléctricas: YOLOv5 en la identificación de puntos calientes mediante imágenes térmicas

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Resumen

Las subestaciones son instalaciones clave dentro de un sistema eléctrico; las fallas intempestivas tienden a causar baja calidad y efectos negativos del suministro eléctrico. Un indicador temprano de posibles fallas en los equipos eléctricos es la aparición de puntos calientes; por lo que su detección y posterior corrección programada evita incurrir en fallas mayores y paradas de operación innecesarias. En esta investigación se realizaron 64 experimentos del algoritmo YOLOv5, con la finalidad de proponer un mecanismo automatizado de visión por computadora para la detección de puntos calientes en imágenes térmicas de subestaciones eléctricas. Los mejores resultados muestran un valor mAP de 81,99 %, los cuales se obtuvieron con el algoritmo YOLOv5m y la aplicación de transfer learning. Estos resultados dejan una base para profundizar y mejorar el desempeño del algoritmo, variando otros hiperparámetros a los considerados en el presente estudio.

Detalles del artículo

Sección
Ingeniería Eléctrica

Referencias

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