农村微电网退化故障率模型综述:评估模型复杂性和数据广泛性

Tinton Dwi Atmaja, Dalila Mat Said, S. M. Idrus, A. Fudholi, M. H. Habibuddin, A. Rajani
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引用次数: 0

摘要

农村地区的微电网对于向偏远社区提供可靠和可持续的电力至关重要。这些微电网的恶化可能导致停电和效率下降。故障率模型是一种用于预测和减轻恶化风险的工具。本研究旨在全面概述农村微电网系统退化的各种故障率模型,重点评估模型的复杂性和数据的广泛性。根据故障率模型的复杂性和数据需求,对14种故障率模型进行了分析。复杂程度分为四个等级,从简单到专家。数据的广泛性分为四个级别,从基础到专家。结果表明,模型的复杂性和广泛性存在显著差异,有些模型比其他模型更适合某些类型的微电网系统。该研究还强调了在为特定的微电网系统选择模型时考虑模型的复杂性和广泛性的重要性。本研究为决策者、微电网工程师和微电网运营商选择最适合其农村微电网系统的故障率模型提供了有价值的见解。研究结果强调需要考虑模型的复杂性和广泛性,以确保其在预测和减轻恶化风险方面的有效性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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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