Abstract

The resilience of power distribution systems is crucial for maintaining the stability and functionality of modern societies. The proximity of natural vegetation to power lines poses significant risks, particularly when combined with adverse weather events. This review paper examines state-of-the-art methods for detecting and managing tree proximity to power distribution lines using advanced machine learning (ML) techniques, including deep learning (DL) applied to remote sensing data. The complex interactions between adverse weather conditions and power outages caused by tree encroachment are explored. The potential of AI-driven monitoring systems to enhance vegetation management strategies, thereby mitigating the risks associated with tree-related power outages, is underlined. A significant gap in the literature is identified, with few studies specifically addressing the application of ML/DL for dynamic monitoring of tree proximity to power lines. A detailed comparative analysis of existing methodologies is provided, emphasizing the unique contributions and limitations of current approaches. Future research directions, including the development of more sophisticated ML/DL models and the integration of multi-sensor data, are outlined. This review serves as a critical resource for researchers, utility managers, and policymakers aiming to improve the resilience and reliability of power infrastructure management.