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

Abstract

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 Ò 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 Ò , 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.

A Comparative Study Between Bayesian Network and Hybrid Statistical Predictive Models for Proactive Power System Network Resilience Enhancement Operational Planning

“…where R is the resistance capacity, W is the wind load, 𝛿 is the mean modulus of rupture (MOR) of the DSN poles (r), and ground line (𝛿 𝑔 ) is the tensile stress of the DSN pole at the ground line, 𝑀 𝑔 is the bending moment at the ground line, Z represents pole section modulus, and D represents the diameter of the pole at the ground line. The detailed modelling of the designated fibre stress due to HS on various wooden poles in the DSN has been proposed in [28], while the yardstick for quantifying the resilience of the power utility DSN's poles is proposed in [29]. In the BN-DSN model, an all-inclusive CPD related to hurricane event forecast is utilized to define the DSN line fault forecast problem formulation.…”

“…The developed SCENRED program in GAMS [44], was applied to the modified 48-bus DSN line damages scenarios predicted from combined statistical DSN's line FC-MCS model, to reduce its predicted 1000-line fault scenarios to 10 with their corresponding optimum probabilities of system line fault for each reduced system line fault scenario generated. See (31)(32), (32)(33), , (34)(35), (35)(36), (35)(36)(37), (37)(38), (39)(40), (40)(41), (41)(42), (42)(43), (44)(45), (44)(45)(46), (46)(47)(48), (47)(48) 0.0570 Sc9 (23)(24), (27)(28), (28)(29), (31)(32), (32)(33), , (34)(35), (35)(36), (35)(36)…”

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

“…In the present study, the UAV technology was adopted to collect utility pole images, which was intended to overcome the shortcomings of the manual or vehicle-based collection methods used in the previous work (Alam et al 2019). The adoption of the UAV technology has the following advantages: (1) The gimbal stabilizer of the UAV is able to ensure that the UAV camera can capture highquality images and videos without vibration or shaking, which is crucial for accurate measurement of utility pole angles.…”

“…The support vector machine technique has already proved its well-established advantages over other classification methods. Despite many possible applications and usefulness, SVM is not widely used in the h), without considering cable effect (Alam et al 2019). However, in this study, the inclination angle is combined with four types of moments to provide a more comprehensive utility pole condition classification to calculate the overall tensile stress on a pole.…”

“…However, the existing pole-by-pole inspection requires much time and effort and is errorprone. Alam et al (2019) proposed a photogrammetry-based methodology for measuring pole inclination angles to determine the health conditions of electric poles. The present study expands on Alam et al (2019) to overcome the perceived limitations in terms of the collection and processing of pole image data.…”