准确预测能源系统资产的健康状况

2018 IEEE International Symposium on Circuits and Systems (ISCAS) Pub Date : 2018-05-27 DOI:10.1109/ISCAS.2018.8351842

Wenshuo Tang, M. Andoni, V. Robu, D. Flynn

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引用次数: 7

摘要

在本文中，我们提出了对数据驱动的预测及其与能源系统弹性的相关性的回顾。当应用于大型生命周期数据集时，通过相关向量机(RVM)模型利用数据分析对锂离子电池进行数据驱动的剩余使用寿命预测，其精度在5%以内。结果表明，由于预测模型的敏捷性及其准确性，预测和健康管理方法对弹性和可持续能源系统至关重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Accurately Forecasting the Health of Energy System Assets

In this paper we present a review into data driven prognostics and its relevance to resilience in energy systems. A data driven remaining useful life prediction for Li-ion batteries utilizing data analysis via a relevance vector machine (RVM) model is shown to be within 5% accuracy when applied to large lifecycle datasets. Results demonstrate that due to the agile nature of prognostic models and their accuracy, prognostics and health management methods will be vital to resilient and sustainable energy systems.

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2018 IEEE International Symposium on Circuits and Systems (ISCAS)

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