Fault detection of wind turbine system based on data-driven methods: a comparative study

被引：5

作者：

Elshenawy, Lamiaa M. ^{[1
,2
]}

Gafar, Ahmed A. ^{[1
]}

Awad, Hamdi A. ^{[1
]}

Abouomar, Mahmoud S. ^{[1
]}

机构：

[1] Menoufia Univ, Fac Elect Engn, Ind Elect & Control Engn, Menoufia 32952, Egypt

[2] AlRyada Univ Sci & Technol, Fac Comp & Artificial Intelligence, Sadat City 32897, Egypt

来源：

NEURAL COMPUTING & APPLICATIONS | 2024年 / 36卷 / 17期

关键词：

Fault detection; Statistical process monitoring; Data-driven methods; Wind turbine system; LATENT STRUCTURES; DIAGNOSIS; REGRESSION; MODEL; COMPONENTS; STRATEGY; CHARTS;

D O I：

10.1007/s00521-024-09604-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fault detection plays a crucial role in ensuring the safety, availability, and reliability of modern industrial processes. This study focuses on data-driven fault detection methods, which have gained significant attention across various industrial sectors due to the rapid development of industrial automation technologies and the availability of extensive datasets. The objectives of this paper are to comprehensively review and present the theoretical foundations of widely used data-driven fault detection approaches. Specifically, these approaches are applied to fault detection in wind turbine systems, with performance evaluation conducted using multiple statistical measures. The data utilized in this study were collected from a simulated benchmark of a wind turbine system. The data-driven methods are tested under the assumption that the wind turbine operates in a steady-state region. Additionally, a comparative study is conducted to identify and discuss the primary challenges associated with the practical application of these methods in real-world scenarios. Simulation results show the effectiveness and efficacy of data-driven approaches concerning the sensitivity and robustness of wind turbine sensor faults as applied in practical industrial environments.

引用

页码：10279 / 10296

页数：18

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