Renewable Energy Systems Prognostics and Health Management: A Review of Recent Advances

被引：1

作者：

Saidi, Lotfi ^{[1
,2
,3
]}

Benbouzid, Mohamed ^{[3
,4
]}

机构：

[1] Univ Tunis, ENSIT Lab Signal Image & Energy Mastery SIME, LR 13ES03, Tunis 1008, Tunisia

[2] Univ Sousse, Higher Sch Sci & Technol Hammam Sousse, Sousse, Tunisia

[3] Univ Brest, UMR CNRS 6027, IRDL, F-29238 Brest, France

[4] Shanghai Maritime Univ, Shanghai 201306, Peoples R China

来源：

IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY | 2021年

关键词：

Bearing; Data acquisition; Health indicator; Machine learning; Power converter; Prognostics and health management; Prognostics approaches; Remaining useful hie prediction; Wind turbine generators; KALMAN FILTER; STATE; PREDICTION; MODEL;

D O I：

10.1109/IECON48115.2021.9589308

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper aims to highlight the methods of predicting the future behavior of a system's health and the remaining useful life to determine an appropriate maintenance schedule. Indeed, prognostic and health management techniques dedicated to renewable energy systems with special reference to wind turbine generators and papers published in the last ten years, are presented. Therefore some applications of prognostics in renewable energy systems, including power converter devices, batteries capacity degradation, and damage in wind turbines' high-speed shaft bearings, are highlighted.

引用

页数：6

共 42 条

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