4.5 Article

An integrated damage modeling and assessment framework for overhead power distribution systems considering tree-failure risks

Journal

STRUCTURE AND INFRASTRUCTURE ENGINEERING
Volume 19, Issue 12, Pages 1745-1760

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/15732479.2022.2053552

Keywords

Computer vision technique; CNN-based image classifier; tree failure risk; physicsbased damage modeling; machine learning; strong winds; fragility analysis; power distribution systems

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An integrated damage modeling and assessment framework is proposed for the overhead power distribution system (OPDS) considering tree failure risks. The geographical information of trees is extracted using computer vision techniques, and tree failure risk models are developed using tree geographical information combined with other factors. The failure probability of the pole is obtained using physics-based modeling, and the poles and wires are connected for system reliability assessment using connectivity-based theory.
The overhead power distribution system (OPDS) is vulnerable to strong winds, such as hurricanes. Due to the challenges of including tree damage risks to the OPDS, tree failures are usually ignored in the risk assessment of the OPDS against strong winds. In the present study, an integrated damage modeling and assessment framework for the OPDS is proposed considering tree failure risks. The geographical information of trees surrounding the OPDS is extracted from satellite images using computer vision techniques, including CNN-based (convolutional neural network) image classifier and sliding window approach. The tree failure risk models are developed using tree geographical information in conjunction with tree height data, tree allometry and finite element analysis. With further integration of the conditional probability failure of poles under fallen tree impacts, the pole's failure probability considering the combined wind and fallen trees is obtained using series system reliability analysis. The failure probability of the pole is obtained using physics-based modeling facilitated by Bayesian regularisation neural network (BRNN) algorithm. The poles and wires are connected for system reliability assessment using connectivity-based theory. When the wind direction is 300 degrees counter-clockwise from the east and the wind speed is 57 m/s, tree-failure can introduce 68.6% differences in OPDS' failure probabilities compared with that without consideration of fallen trees.

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