A review of failure rate models for deterioration in rural microgrids: Evaluating model complexity and data extensiveness
Microgrids in rural areas is crucial for providing reliable and sustainable electricity to remote communities. The deterioration of these microgrids can result in power outages and decreased efficiency. A failure rate model is a tool used to predict and mitigate the risk of deterioration. This stud...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTM Press
2023
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| Subjects: | |
| Online Access: | http://eprints.utm.my/108563/1/DalilaMatSaid2023_AReviewofFailureRateModelsforDeterioration.pdf http://eprints.utm.my/108563/ http://dx.doi.org/10.11113/elektrika.v22n1.435 |
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| Summary: | Microgrids in rural areas is crucial for providing reliable and sustainable electricity to remote communities. The deterioration of these microgrids can result in power outages and decreased efficiency. A failure rate model is a tool used to predict and mitigate the risk of deterioration. This study aims to provide a comprehensive overview of various failure rate models for deterioration in rural microgrid systems, with a focus on evaluating the model complexity and data extensiveness. A total of fourteen failure rate models were analyzed based on their complexity and data requirements. Complexity was evaluated in four levels, ranging from simple to expert. Data extensiveness was evaluated in four levels, ranging from basic to expert. The results show that the complexity and extensiveness of the models vary significantly, with some models being more appropriate for certain types of microgrid systems than others. The study also highlights the importance of considering the complexity and extensiveness of a model when selecting it for a particular microgrid system. This study provides valuable insights for policymakers, microgrid engineers, and microgrid operators in selecting the most appropriate failure rate model for their rural microgrid systems. The findings emphasize the need to consider the complexity and extensiveness of the model to ensure its effectiveness and efficiency in predicting and mitigating the risk of deterioration. |
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