An Optimal Algorithmic Approach to Efficiently Automate Fault Isolation and Service Restoration on an Arbitrary Distribution Feeder System

Granberg, Daniel; Pinney, David; Eldali, Fathalla

doi:10.1109/tpwrd.2021.3121226

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Reliability measures the continued success of a system, while resilience measures the insensitivity of the system to disruptions 19 . Whereat fault isolation is an important aspect in achieving internal reliability 20 , which in turn increases resilience. Holling pointed out a principal difference between resilience and stability 8 , stability is an ability of a system to return to equilibrium with the least fluctuation after a temporary disruption.…”

Section: Resilience Backgroundmentioning

confidence: 99%

Analysis of quantitative metrics for assessing resilience of human-centered CPPS workstations

Aruväli

Marchi

Rauch

2023

Sci Rep

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Manufacturing companies’ preparedness level against external and internal disruptions is complex to assess due to a lack of widely recognized or standardized models. Resilience as the measure to characterize preparedness against disruptions is a concept with various numerical approaches, but still lacking in the industry standard. Therefore, the main contribution of the research is the comparison of existing resilience metrics and the selection of the practically usable quantitative metric that allows manufacturers to start assessing the resilience in digitally supported human-centered workstations more easily. An additional contribution is the detection and highlighting of disruptions that potentially influence manufacturing workstations the most. Using five weighted comparison criteria, the resilience metrics were pairwise compared based on multi-criteria decision-making Analytic Hierarchy Process analysis on a linear scale. The general probabilistic resilience assessment method Penalty of Change that received the highest score considers the probability of disruptions and related cost of potential changes as inputs for resilience calculation. Additionally, manufacturing-related disruptions were extracted from the literature and categorized for a better overview. The Frequency Effect Sizes of the extracted disruptions were calculated to point out the most influencing disruptions. Overall, resilience quantification in manufacturing requires further research to improve its accuracy while maintaining practical usability.

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Section: Resilience Backgroundmentioning

confidence: 99%