Dynamic modeling of the effects of vegetation management on weather-related power outages

Taylor, William O.; Watson, Peter L.; Cerrai, Diego; Anagnostou, Emmanouil N.

doi:10.1016/j.epsr.2022.107840

Cited by 20 publications

(3 citation statements)

References 30 publications

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“…Taylor et al [15] evaluated the impact of vegetation management to reduce ESIs during storms. A machine learning model was applied, where data on vegetation management, soil cover, climate data, and electrical infrastructure were inserted.…”

Section: A Literature Reviewmentioning

confidence: 99%

Estimation of Vulnerable Areas to Faults Caused by Tree Vegetation in Power Distribution Systems

Santos,

Faria,

Boschi

et al. 2024

IEEE Access

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Faults in power distribution feeders cause damage to power utilities due to the deterioration of reliability and power quality indexes and the displacement of field maintenance teams to replace or repair power grid equipment. Additionally, consumer units have energy supply interruptions for an undetermined time. Studies in specialized literature usually detect, classify, and locate faults after they occur. In contrast, preventing faults by estimating areas vulnerable to them is crucial to mitigate all inconveniences and additional costs after they occur. Tree vegetation is an essential factor contributing to faults. In this sense, an enhanced method for tree vegetation mapping by areas is developed using multilayer perceptron neural networks trained on high-resolution images from Google Earth. A geographic space is incorporated to estimate the regions vulnerable to failures due to tree vegetation. Geographically weighted spatial analysis is applied from local variables aggregated by areas. Spatial data analysis is used to real faults and tree vegetation data from a medium-sized Brazilian city via QGIS and R programming environments. As a result, thematic maps are produced with the areas whose feeders are vulnerable to faults, where there is a moderate positive correlation by regions between the faults in distribution transformers and tree vegetation in the northeast and southwest areas of the city under study.

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Section: A Literature Reviewmentioning

confidence: 99%

Estimation of Vulnerable Areas to Faults Caused by Tree Vegetation in Power Distribution Systems

Santos,

Faria,

Boschi

et al. 2024

IEEE Access

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“…According to the group analysis, AI could contribute positively to enhancing access to electricity (7. (e.g., solar roofs) and match them according to the population and available resources in the area [161][162][163][164][165][166].…”

Section: Group 3: Ai As a DImentioning

confidence: 99%

Operationalizing Digitainability: Encouraging mindfulness to harness the power of digitalization for sustainable development

Gupta¹,

Zeballos²,

Castro³

et al. 2022

Preprint

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Digitalization is globally transforming the world with profound implications. It has enormous potential to foster progress toward sustainability. However, in its current form, digitalization also continues to enable and encourage practices with numerous unsustainable impacts affecting our environment, ingraining inequality, and degrading quality of life. There is an urgent need to identify such multifaceted impacts holistically. Impact assessment of digital interventions (DIs) leading to digitalization is important specifically for Sustainable Development Goals(SDGs). Action is required to understand the pursuit of short-term gains toward achieving long-term value-driven sustainable development. We need to understand the impact of DIs on various actors and in diverse contexts. A holistic understanding of the impact it creates will help us align it with visions of sustainable development and identify potential measures to mitigate negative short and long-term impacts. The recently developed Digitainability Assessment Framework (DAF) unveils the impact of DIs with an in-depth context-aware assessment and offers an evidence-based impact profile of SDGs at the indicator level. We performed the impact assessment of diverse technologies using DAF. This paper summarizes the insights from the Digitainable Spring School 2022 on "Sustainability with Digitalization and Artificial Intelligence," one of whose goals was to operationalize the DAF as a tool in the action learning process with diverse professionals in the field of digitalization and sustainability. The DAF guides a holistic context-aware process formulation for a given DI. An evidence-based evaluation within the DAF protocol benchmarks a specific DI’s impact against the SDG indicators framework. The operationalization of the DAF was carried out by looking at four different DIs: smart home technologies (SHT) for energy efficiency, blockchain for food security, artificial intelligence for land use cover and changes (LUCC), and big data for international law. Each of the four studies addresses different DIs for digitainability assessment using different techniques for a diverse group of indicators, demonstrating the potential of the DAF but also outlining the existing data gaps that limit a comprehensive analysis.

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“…Confounding factor analysis can primarily be performed at the circuit level, which is at coarser granularity, as well as at the device exposure zone (DEZ) level (the outage locations correspond to isolating devices each of which protect a section of power line called a device exposure zone). Most of the studies have been conducted at the circuit level (Taylor et al, 2022, Zhu et al, 2007. A DEZ level modeling approach will account, in addition to forests related factors, multitudes of other environmental factors, soil and terrain (soil, slope and aspect), utility infrastructure related variables (overhead length, overhead to underground powerline length), and vegetation management practices (e.g., tree trimming and hazard tree removal).…”

Section: Introductionmentioning

confidence: 99%

Geospatial Modeling of Roadside Vegetation Risk on Distribution Power Lines in Connecticut

Wedegedara

Witharana

Joshi

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Roadside trees cause almost 90% of the power outages in the forested Northeastern US. Management of roadside vegetation risk on electrical infrastructure demands timely and accurate information on forest conditions. Tasking conventional ground-based scouting methods along thousands of kilometers of powerlines in a repeated fashion are labor-/cost-/time-intense. Geospatial and earth observation (EO) technologies serve as cost-effective tools in monitoring, inspecting, and managing utility corridors. EO technologies, from drones, aircraft, to satellites can efficiently acquire information over large areas at regular intervals while probing forest physical structure and health conditions. LiDAR is a useful data stream for modeling terrain conditions and estimation of multiple forest inventory variables that explain the physical structure of the forest. Various EO imagery provides information on bio-physical characteristics of trees that affect forest health at finer granularity. The goal of this study is to combine multiple environmental variables to develop a spatially-explicit vegetation risk model using machine learning algorithms. Some of the key inputs used in our analysis include LiDAR-derived tree-related variables (e.g., tree height, proximity pixels, canopy cover), LiDAR-derived terrain data (slope, aspect, topographic index), soil characteristics, vegetation management data (tree trimming methods), infrastructure data (wire type), and power outages reported from 2005 to 2017 in Connecticut. Findings of this research will be vital in informing vegetation management decision-making processes, which eventually reduce power outages and the cost of utility corridor maintenance.

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Dynamic modeling of the effects of vegetation management on weather-related power outages

Cited by 20 publications

References 30 publications

Estimation of Vulnerable Areas to Faults Caused by Tree Vegetation in Power Distribution Systems

Estimation of Vulnerable Areas to Faults Caused by Tree Vegetation in Power Distribution Systems

Operationalizing Digitainability: Encouraging mindfulness to harness the power of digitalization for sustainable development

Geospatial Modeling of Roadside Vegetation Risk on Distribution Power Lines in Connecticut

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