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
2009 ◽  
Vol 13 (1) ◽  
pp. 1-39 ◽  
Author(s):  
Z. Hameed ◽  
Y.S. Hong ◽  
Y.M. Cho ◽  
S.H. Ahn ◽  
C.K. Song

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1305
Author(s):  
Magnus F. Asmussen ◽  
Jesper Liniger ◽  
Henrik C. Pedersen

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.


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 ◽  
...  

2022 ◽  
Author(s):  
P.B. Dao

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.


2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Konrad Zolna ◽  
Phong B. Dao ◽  
Wieslaw J. Staszewski ◽  
Tomasz Barszcz

Monitoring of trends and removal of undesired trends from operational/process parameters in wind turbines is important for their condition monitoring. This paper presents the homoscedastic nonlinear cointegration for the solution to this problem. The cointegration approach used leads to stable variances in cointegration residuals. The adapted Breusch-Pagan test procedure is developed to test for the presence of heteroscedasticity in cointegration residuals obtained from the nonlinear cointegration analysis. Examples using three different time series data sets—that is, one with a nonlinear quadratic deterministic trend, another with a nonlinear exponential deterministic trend, and experimental data from a wind turbine drivetrain—are used to illustrate the method and demonstrate possible practical applications. The results show that the proposed approach can be used for effective removal of nonlinear trends form various types of data, allowing for possible condition monitoring applications.


Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 260
Author(s):  
Nuno M. A. Freire ◽  
Antonio J. Marques Cardoso

Research on fault detection (FD) and condition monitoring (CM) of rotating electrical generators for modern wind turbines has addressed a wide variety of technologies. Among these, permanent magnet synchronous generators (PMSGs) and the analysis of their electromagnetic signatures in the presence of faults deserve emphasis in this paper. PMSGs are prominent in the offshore wind industry, and methods for FD and CM of PMSGs based on electromagnetic measurements are extensively discussed in academia. This paper is a concise review of FD and CM in wind turbines and PMSGs. Terminology and fundamentals of PMSG’s operation are introduced first, aiming to offer an easy read and good reference to a broad audience of engineers and data scientists. Experience and research challenges with stator winding failures are also discussed.


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