A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines

Urmeneta, Jon; Izquierdo, Juan Santiago Murgui; Leturiondo, Urko

doi:10.1016/j.renene.2023.01.035

Cited by 4 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The initial module of the block focuses on efficient data processing methods of the timeseries: filtering, anomaly cleaning, or data imputation techniques. It also incorporates the use of merged condition indicators through techniques like principal component analysis (PCA) [19], investigating the extraction of features from various sources, such as vibration signals or acoustic emissions, in different domains like time, frequency, and time-frequency. These features, including basic statistical indicators, spectral characteristics, and time-frequency representations, are crucial for future problem detection, diagnosis, and prognosis.…”

Section: Analysis Blockmentioning

confidence: 99%

“…On the other hand, for an overall assessment of a WT at the system level, a CMbased performance assessment can be addressed. This contributes to the expanding field of performance-based maintenance by introducing a novel approach rooted in CM, specifically focusing on non-binary failures [19]. No comprehensive knowledge is required for the interpretation of the results, as a consequence, it could be used as a simple tool for monitoring the performance of WTs [61].…”

Section: Analysis and CM Challengesmentioning

confidence: 99%

See 1 more Smart Citation

Challenges on prognostics and health management for wind turbine components

Cuesta,

Leturiondo,

Vidal

et al. 2024

J. Phys.: Conf. Ser.

Self Cite

View full text Add to dashboard Cite

This paper explores the applicability of prognostics and health management (PHM) for wind turbines (WTs), presenting the PHM approach along with challenges and opportunities in the context of WT components. First, the PHM framework is introduced, consisting of three blocks: observation, analysis, and action. Critical components and failure modes for WTs are identified, and data acquisition strategies using supervisory control and data adquisition (SCADA) and condition monitoring (CM) data are discussed. Prognostics, specifically remaining useful life (RUL) estimation, employs physics model-based, data-driven, and hybrid models. Finally, challenges and opportunities related to data, analysis and CM, and developing RUL prediction models have been found. Data challenges include data standardization, limited public datasets, and data quality issues. Analysis and CM challenges address new sensorless and non-intrusive techniques, as well as the fusion of data sources. Prognostics model challenges involve uncertainty management, interpretability issues, and the need for online updates. Addressing challenges requires incorporating physical knowledge, utilizing transfer learning, and improving online RUL prediction methods.

show abstract

Section: Analysis Blockmentioning

confidence: 99%

Section: Analysis and CM Challengesmentioning

confidence: 99%

Challenges on prognostics and health management for wind turbine components

Cuesta,

Leturiondo,

Vidal

et al. 2024

J. Phys.: Conf. Ser.

Self Cite

View full text Add to dashboard Cite

show abstract

“…According to Equation (3), the probability of each state at any time can be obtained. The sum of the state probability of each turbine component is given as Equation (5).…”

Section: A Dpm Turbine Systemmentioning

confidence: 99%

“…Many scholars have researched the maintenance of wind turbine systems [2][3][4][5], but few researchers focus on the grouping maintenance of wind turbine systems due to their components' complexity. A wind turbine system plays an important part in ensuring the stable energy output of a wind farm [6].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Grouping Maintenance Policy for Improving Reliability of Wind Turbine Systems Considering Variable Cost

Dui

Zhang

2023

Mathematics

View full text Add to dashboard Cite

Wind farms have gained wide attention due to unlimited resources and clean energy. Considering that wind turbine systems are always in harsh conditions, subsystem failures could reduce the reliability of wind turbine systems. At present, the maintenance behaviors for wind turbine systems are various (e.g., corrective maintenance, preventive maintenance) when reliability is reduced below the threshold. Considering the maintenance cost and downtime, it is impossible to repair each component in a timely manner. One of the key problems is dividing components into maintenance groups to improve maintenance efficiency. In this paper, a grouping maintenance policy considering the variable cost (GMP-VC) is proposed to improve direct-drive permanent magnet (DPM) turbine systems. Grouping modes are proposed to fully consider the stated transition probability of turbine components and the variable cost of turbine systems. A maintenance model is formulated to select components as members of the group based on a RIM-VC index. An instance is given to verify the proposed GMP-VC method. The result indicates that the proposed maintenance policy may save maintenance costs over baseline plans.

show abstract

Real-time monitoring, fault prediction and health management for offshore wind turbine systems

Gao,

Odgaard

2023

Renewable Energy

View full text Add to dashboard Cite

A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines

Cited by 4 publications

References 34 publications

Challenges on prognostics and health management for wind turbine components

Challenges on prognostics and health management for wind turbine components

Grouping Maintenance Policy for Improving Reliability of Wind Turbine Systems Considering Variable Cost

Real-time monitoring, fault prediction and health management for offshore wind turbine systems

Contact Info

Product

Resources

About