Framework for Measuring the Resilience of Utility Poles of an Electric Power Distribution Network

The utility poles of an electric power distribution system are frequently damaged by wind-related disasters.This study notes that the wooden poles are particularly vulnerable to such disasters and the failures of the poles can cause a network-level failure leading to short-or longterm power outages. To mitigate the problem, this study proposes a framework for measuring the resilience of the wooden utility poles based on the angular deflection of a pole due to the wind force. Given the existing inclination angle of a pole, the angular deflection is measured by finite element analysis using ANSYS~? Workbench^1 to determine the resilience area under various wind speeds. For this, the conditions of load and support for a pole, which are called boundary conditions in ANSYS~?, are generated. The proposed framework also includes an approach to cost–benefit analysis that compares different strategies for corrective action. The results of the case study in which the framework was applied show that the proposed framework can be effectively utilized by electric power distribution companies to increase the resilience of their systems.

Automatic Assessment and Prediction of the Resilience of Utility Poles Using Unmanned Aerial Vehicles and Computer Vision Techniques

The utility poles of electric power distribution lines are very vulnerable to many natural hazards, while power outages due to pole failures can lead to adverse economic and social consequences. Utility companies,therefore, need to monitor the conditions of poles regularly and predict their future conditions accurately and promptly to operate the distribution system continuously and safely.This article presents a novel pole monitoring method that uses state-of-the-art deep learning and computer vision methods to meet the need. The proposed method automatically captures the current pole inclination angles using an unmanned aerial vehicle. The method calculates the bending moment exerted on the poles due to wind and gravitational forces, as well as cable weight, to compare it with the moment of rupture. The method also includes a machine learning-based model that is built by using a support vector machine to predict the resilience conditions of a pole after a wind event in a faster manner. The three modules of the proposed method are effective tools to classify pole conditions and are expected to enable utility companies to increase the resilience of their systems.

Resilience Quantification and Its Application to a Residential Building Subject to Hurricane Winds

In order to overcome negative consequences of a disaster, buildings and infrastructures need to be resilient. After a disaster occurs, they must get back to their normal operations as quickly as possible. Buildings and infrastructures should incorporate both pre-event(preparedness and mitigation) and post-event(response and recovery) resilience activities to minimize negative effects of a disaster. Quantitative approaches for measuring resilience for buildings and infrastructures need to be developed. A proposed methodology for quantification of resilience of a given building type based on different hurricane categories is presented. The formulation for the resilience quantification is based on a model embedding several distinct parameters(for example, structural loss ratios, conditional probabilities of exceeding for damage states, estimated and actual recovery times, wind speed probability). The proposed resilience formulation is applied to a residential building type selected from HAZUS.iNumerical results of resilience for the selected residential building type against Category 1, 2, and 3 hurricanes are presented in a dashboard representation. Resilience performance indicators between different types of buildings, which are identical except for their roof types, have been evaluated in order to present applicability of the proposed methodology.