Methodology for fault risk level estimation of overhead lines using open data

Abstract

Electricity distribution networks are a significant component of any energy system and due to various environmental factors, these systems encounter fault risks that may lead to substantial power outages. This thesis presents an immense geospatial analysis and statistical analysis of the risk estimation of overhead lines using open data. The analysis includes multiple types of datasets such as the canopy height model (CHM), tree volume and diameter, major species of the trees, crown snow load, and wind speed data. For analysis, the algorithm was developed in Python programming language and Quantum Geographic Information System (QGIS) open-source software to link and assess the data for risk location in the specified areas. Various statistical and geospatial techniques are applied to figure out the relation between overhead lines and all data sets. The key finding of this thesis shows the essential role of tree, snow, and wind variables which are contributing to the risk of power outages that not only pose economic losses but also public inconvenience and safety risks. The developed algorithm allows risk estimation in high resolution, and it is scalable to any large geographical areas and as well as yields a better class of analysis for utility companies to implement precise actions for maintenance and preventive measures.