Publicado:
2024-06-23Número:
Vol. 18 Núm. 1 (2024)Sección:
Visión InvestigadoraReliability assessment in complex systems using updated fault rate estimation
Evaluación de confiabilidad en sistemas complejos mediante la estimación actualizada de la tasa de fallas
Palabras clave:
Electrical systems, Estimation, Failure rate, Reliability, Time series (en).Palabras clave:
Confiabilidad, estimación, Series de tiempo, Sistemas eléctricos, Tasa de fallas (es).Descargas
Resumen (en)
The expansion and operation of the electrical network plays a significant role in ensuring service quality. Keeping the components operational requires an integrated, optimal and sustainable system focused on reliability analysis to ensure the efficiency and availability in complex systems. This paper explores a methodology to improve reliability analyses on power system components, using univariate data analysis and time series regression to develop an updated fault rate estimation. An exploratory data analysis is developed to understand the behavior and uncertain nature of the variables under study. The Simple Exponential Smoothing (SES) model and the Autoregressive Integrated Moving Average (ARIMA) are implemented to analyze and estimate the behavior of the failure rate of some electrical distribution transformers over time, where the variability of reliability is observed as the failure rate varies over time, due to external factors.
Resumen (es)
La expansión y operación de la red eléctrica trae consigo grandes retos para garantizar la calidad del servicio. Mantener operativos los activos requiere de un sistema integrado, óptimo y sostenible enfocado en los análisis de confiabilidad para asegurar la eficiencia y disponibilidad de los sistemas complejos. Este documento, basado en un enfoque analítico, explora cómo mejorar los análisis de confiabilidad en los componentes de los sistemas eléctricos de potencia, mediante el análisis de datos univariante, así como metodologías de regresión de series temporales para desarrollar una estimación actualizada de la tasa de fallas. Inicialmente, se desarrolla un análisis exploratorio de datos para comprender el Autoregressive Integrated Moving Average (ARIMA), suavizado exponencial simple (SES) se implementan para analizar y estimar el comportamiento de la tasa de fallas de algunos transformadores de distribución a lo largo del tiempo, donde se observa la variabilidad de la confiabilidad a medida que la tasa de fallas presenta variaciones en el tiempo, debido a factores externos.
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