Cointegration Modelling for Health and Condition Monitoring of Wind Turbines - An Overview

2022 ◽  
Author(s):  
P.B. Dao
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Control Charts ◽  
Failure Detection ◽  
Short Review ◽  
Early Failure ◽  
Promising Tool ◽  
Condition Monitoring Systems ◽  
Scientists And Engineers

Abstract. The cointegration method has recently attracted a growing interest from scientists and engineers as a promising tool for the development of wind turbine condition monitoring systems. This paper presents a short review of cointegration-based techniques developed for condition monitoring and fault detection of wind turbines. In all reported applications, cointegration residuals are used in control charts for condition monitoring and early failure detection. This is known as the residual-based control chart approach. Vibration signals and SCADA data are typically used with cointegration in these applications. This is due to the fact that vibration-based condition monitoring is one of the most common and effective techniques (used for wind turbines); and the use of SCADA data for condition monitoring and fault detection of wind turbines has become more and more popular in recent years.

scholarly journals Alternative fault detection and diagnostic using information theory quantifiers based on vibration time-waveforms from condition monitoring systems: Application to operational wind turbines

2021 ◽  
Vol 164 ◽  
pp. 1183-1194
Author(s):  
Gustavo de Novaes Pires Leite ◽  
Guilherme Tenório Maciel da Cunha ◽  
José Guilhermino dos Santos Junior ◽  
Alex Maurício Araújo ◽  
Pedro André Carvalho Rosas ◽  
...  
Keyword(s):  
Information Theory ◽  
Fault Detection ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Monitoring Systems ◽  
Condition Monitoring Systems ◽  
Vibration Time

Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data

2018 ◽  
Vol 116 ◽  
pp. 107-122 ◽  
Author(s):  
Phong B. Dao ◽  
Wieslaw J. Staszewski ◽  
Tomasz Barszcz ◽  
Tadeusz Uhl
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Cointegration Analysis

Knowledge-based Noise Analysis: A Promising Tool for Early Failure Detection in Nuclear Power Plants

1991 ◽  
Vol 24 (6) ◽  
pp. 383-390
Author(s):  
J. Bokor ◽  
A. Edelmayer ◽  
A. Soumelidis ◽  
M. Tanyi ◽  
P. Gáspár ◽  
...  
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Noise Analysis ◽  
Failure Detection ◽  
Early Failure ◽  
Knowledge Based ◽  
Promising Tool

scholarly journals Remote data acquisition for condition monitoring of wind turbines

2015 ◽  
Vol 6 (2) ◽  
pp. 10
Author(s):  
Bavo De Maré ◽  
Jacob Sukumaran ◽  
Mia Loccufier ◽  
Patrick De Baets
Keyword(s):  
Data Acquisition ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Good Condition ◽  
Offshore Wind ◽  
Monitoring Systems ◽  
Offshore Wind Turbines ◽  
Condition Monitoring Systems ◽  
Remote Data

While the number of offshore wind turbines is growing and turbines getting bigger and more expensive, the need for good condition monitoring systems is rising. From the research it is clear that failures of the gearbox, and in particular the gearwheels and bearings of the gearbox, have been responsible for the most downtime of a wind turbine. Gearwheels and bearings are being simulated in a multi-sensor environment to observe the wear on the surface.

scholarly journals The specification and testing of a Horizontal Axis Tidal Turbine Rotor Monitoring approach

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Matthew Allmark ◽  
Paul Prickett ◽  
Roger Grosvenor ◽  
Carwyn Frost
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Fault Detection ◽  
Condition Monitoring ◽  
Turbulence Intensity ◽  
Turbine Rotor ◽  
Horizontal Axis ◽  
Monitoring Systems ◽  
Tidal Turbine ◽  
Condition Monitoring Systems

The sustainable deployment of Horizontal Axis Tidal Turbines will require effective management and maintenance functions. In part, these can be supported by the engineering of suitable condition monitoring systems. The development of such a system is inevitably challenging, particularly given the present limited level of operational data associated with installed turbines during fault onset. To mitigate this limitation, a computational fluid dynamics model is used to simulate the operational response of a turbine under a known set of fault conditions. Turbine rotor imbalance faults were simulated by the introduction of increasing levels of pitch angle offset for a single turbine blade. The effects of these fault cases upon cyclic variations in the torque developed by the turbine rotor were then used to aid creation of a condition monitoring approach. A parametric tidal turbine rotor model was developed based on the outputs of the computational fluid dynamics models. The model was used to facilitate testing of the condition monitoring approach under a variety of more realistic conditions. The condition monitoring approach showed good performance in fault detection and diagnosis for simulations relating to turbulence intensities of up to 2 %. Finally, the condition monitoring approach was applied to simulations of 10 % turbulence intensity. Under the 10 % turbulence intensity case the rotor monitoring approach was successfully demonstrated in its use for fault detection. The paper closes with discussion of the effectiveness of using computational fluid dynamics simulations extended by parametric models to develop condition monitoring systems for horizontal axis tidal turbine applications.

Condition monitoring and fault detection of wind turbines and related algorithms: A review

2009 ◽  
Vol 13 (1) ◽  
pp. 1-39 ◽  
Author(s):  
Z. Hameed ◽  
Y.S. Hong ◽  
Y.M. Cho ◽  
S.H. Ahn ◽  
C.K. Song
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Wind Turbines

scholarly journals A Survey of Deep Learning Approaches on Wind Turbine Condition Monitoring

2021 ◽  
Vol 2132 (1) ◽  
pp. 012015
Author(s):  
Sijia Li
Keyword(s):  
Deep Learning ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Control Charts ◽  
Original Data ◽  
Learning Approaches ◽  
Monitoring Methods ◽  
Effectiveness And Efficiency ◽  
Operational Data

Abstract Current physics-based wind turbine monitoring methods often need extra sensors installed on wind turbines, thus increasing the operation and maintenance (O&M) cost. Besides, physical methods are only effective under some constraints. The real effectiveness needs to be further checked in real conditions. Recent advances in data acquisition systems allow collection of large volumes of operational data of wind turbines. Learning knowledge from the data allows us to do monitoring in another direction. In this paper, a survey of deep learning algorithms applied to wind turbine condition monitoring is given. Compared with original data, more meaning features were extracted through feature extraction of deep learning. Monitoring these new signals, outliers were detected by applying suitable control charts. Several industrial cases confirmed the effectiveness and efficiency of these frameworks.

scholarly journals Fault Detection and Diagnosis Methods for Fluid Power Pitch System Components—A Review

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1305
Author(s):  
Magnus F. Asmussen ◽  
Jesper Liniger ◽  
Henrik C. Pedersen
Keyword(s):  
Fault Detection ◽  
Power Systems ◽  
Wind Energy ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Fault Detection And Diagnosis ◽  
Fluid Power ◽  
Pitch System ◽  
Fluid Power Systems

Wind turbines have become a significant part of the global power production and are still increasing in capacity. Pitch systems are an important part of modern wind turbines where they are used to apply aerodynamic braking for power regulation and emergency shutdowns. Studies have shown that the pitch system is responsible for up to 20% of the total down time of a wind turbine. Reducing the down time is an important factor for decreasing the total cost of energy of wind energy in order to make wind energy more competitive. Due to this, attention has come to condition monitoring and fault detection of such systems as an attempt to increase the reliability and availability, hereby the reducing the turbine downtime. Some methods for fault detection and condition monitoring of fluid power systems do exists, though not many are used in today’s pitch systems. This paper gives an overview of fault detection and condition monitoring methods of fluid power systems similar to fluid power pitch systems in wind turbines and discuss their applicability in relation to pitch systems. The purpose is to give an overview of which methods that exist and to find areas where new methods need to be developed or existing need to be modified. The paper goes through the most important components of a pitch system and discuss the existing methods related to each type of component. Furthermore, it is considered if existing methods can be used for fluid power pitch systems for wind turbine.

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