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Revisión Literaria sobre Temas de Investigación Actuales y Futuros para el Análisis de Huecos de Tensión
A Review on Current and Future Research Topics on Voltage Sags
DOI:
https://doi.org/10.15446/sicel.v12.123230Palabras clave:
Calidad de energía, aprendizaje automático, detección, clasificación, Mitigación, Low-Voltage Ride-Through, evaluación de riesgos, huecos de tensión, Low-Voltage Ride-Through (LVRT), risk assessment (es)Power quality, voltage sag, machine learning (ML), detection, classification, mitigation (en)
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La calidad de la energía se ha convertido en una preocupación crítica en los sistemas eléctricos modernos, donde la proliferación de equipos electrónicos sensibles y la generación distribuida han aumentado la vulnerabilidad a las perturbaciones. Entre estas, los huecos de tensión son una fuente importante de pérdidas económicas, causando el mal funcionamiento de los equipos y paros de producción. Si bien existen técnicas de análisis y mitigación tradicionales, a menudo no logran abordar las complejidades de una red en evolución, revelando importantes brechas de investigación en la clasificación, la regulación y las estrategias de mitigación. Esta revisión sintetiza el estado actual del conocimiento y propone una agenda de investigación prospectiva para guiar futuras contribuciones en el campo. Se exploran cuatro preguntas de investigación clave, centrándose en los desafíos más urgentes. En primer lugar, examinamos el potencial de los modelos de aprendizaje automático (ML) y profundo (DL) aplicados al monitoreo distribuido para detectar y clasificar los huecos de tensión, lo que apoya la detección de fallas y las acciones preventivas en las redes inteligentes. En segundo lugar, exploramos las estrategias de control más efectivas para los Sistemas de Almacenamiento de Energía con Baterías (BESS) y otros recursos de energía distribuida (DERs) para mitigar los huecos de tensión y mejorar la calidad del producto eléctrico. En tercer lugar, investigamos la efectividad de los esquemas de permanencia ante baja tensión (LVRT) y las estrategias de control coordinado para inversores de energía renovable para prevenir fallas en cascada y mejorar la estabilidad de la red. Finalmente, el artículo analiza marcos modernos de modelado estocástico y de riesgo para combinar la sensibilidad de los equipos con análisis robustos de costo-beneficio, proporcionando una justificación económica clara para las inversiones en mitigación. Este artículo proporciona una hoja de ruta crucial para que los investigadores mejoren la resiliencia, la eficiencia y la viabilidad económica de los sistemas de potencia en un panorama energético descentralizado.
Power quality has become a critical concern in modern power systems, where the proliferation of sensitive electronic equipment and distributed generation has increased vulnerability to disturbances. Among these, voltage sags are a major source of economic losses, causing equipment malfunctions and production shutdowns. Although traditional analysis and mitigation techniques exist, they often fail to address the complexities of an evolving grid, revealing significant research gaps in classification, regulation, and mitigation strategies. This review synthesizes the current state of knowledge and proposes a forward-looking research agenda to guide future contributions in the field. Four key research questions are explored, focusing on the most pressing challenges. First, we examine the potential of machine learning (ML) and deep learning (DL) models applied to distributed monitoring to detect and classify voltage sags, supporting fault detection and preventive actions in smart grids. Second, we explore the most effective control strategies for Battery Energy Storage Systems (BESS) and other Distributed Energy Resources (DERs) to mitigate voltage sags and improve power quality. Third, we investigate the effectiveness of Low-Voltage Ride-Through (LVRT) schemes and coordinated control strategies for renewable energy inverters to prevent cascading failures and enhance grid stability. Finally, the article analyzes modern stochastic and risk modeling frameworks to combine equipment sensitivity with robust cost-benefit analyses, providing a clear economic justification for mitigation investments. This article offers a crucial roadmap for researchers to enhance the resilience, efficiency, and economic viability of power systems in a decentralized energy landscape.
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Derechos de autor 2025 Fernando Tavera, Helen Alarcon, Jorge Lechón, Squiel Sachelaridi, Joaquín Caicedo, Andrés Arturo Romero Quete
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
