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- W3203189259 abstract "Climate change has been given significant attention in recent times, calling for more ideas to address issues stemming from extreme weather events. Until climate change can be slowed down and ultimately be reversed, it is an imminent objective to find solutions for the mitigation and prevention of the effects of catastrophic weather events. History has a record of the devastation caused by such events on electrical power infrastructures. While electrical power systems have conventionally been designed and built to weather everyday conditions, confronting cataclysmic high-impact, low-probability events will require more resilient attributes. Power systems are often deemed as the backbone of the operational society, and therefore, the case calling for power infrastructures to be able to withstand critical events is a case carrying compelling weight. The concept of resilience in power systems however, has only emerged in recent times. This study therefore, aims to provide further insights into the area of power system resilience, by focusing on the aftermath of an extreme weather event and how its effects on a power system can be utilized to measure the resilience of the latter. To replicate the random behaviour of weather, windspeeds categorized based on Saffir-Simpson’s hurricane scale, were randomly generated following the concept of Monte-Carlo’s simulation technique, which would then be applied to fragility curves of distribution poles based on NESC’s distribution pole classes. The resilience of the infrastructure is then modelled and assessed by applying the 3 different resilience assessment methods. Finally, evaluations are made to compare the merits and disadvantages of each resilience assessment method." @default.
- W3203189259 created "2021-10-11" @default.
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- W3203189259 date "2020-06-01" @default.
- W3203189259 modified "2023-09-27" @default.
- W3203189259 title "Comparison of different power system resilience assessment methods / Sam Chan Jian How" @default.
- W3203189259 hasPublicationYear "2020" @default.
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