scholarly journals Final Technical Report Recovery Act: Online Nonintrusive Condition Monitoring and Fault Detection for Wind Turbines

2012 ◽  
Author(s):  
Wei Qiao
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Technical Report ◽  
Recovery Act

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

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 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.

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

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 Fault Detection and Condition Monitoring of PMSGs in Offshore Wind Turbines

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 260
Author(s):  
Nuno M. A. Freire ◽  
Antonio J. Marques Cardoso
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Offshore Wind ◽  
Synchronous Generators ◽  
Offshore Wind Turbines ◽  
Permanent Magnet Synchronous Generators ◽  
Concise Review ◽  
Electromagnetic Measurements ◽  
Offshore Wind Industry

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.

scholarly journals Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3018 ◽  
Author(s):  
Yolanda Vidal ◽  
Francesc Pozo ◽  
Christian Tutivén
Keyword(s):  
Support Vector Machines ◽  
Fault Detection ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Principal Component ◽  
Support Vector ◽  
Fault Detection And Classification ◽  
First Choice ◽  
Vector Machines

Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a data-driven multi-fault detection and classification strategy is developed. An advanced wind turbine benchmark is used. The wind turbine we consider is subject to different types of faults on actuators and sensors. The main challenges of the wind turbine fault detection lie in their non-linearity, unknown disturbances, and significant measurement noise at each sensor. First, the SCADA measurements are pre-processed by group scaling and feature transformation (from the original high-dimensional feature space to a new space with reduced dimensionality) based on multiway principal component analysis through sample-wise unfolding. Then, 10-fold cross-validation support vector machines-based classification is applied. In this work, support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results showed that all studied faults were detected and classified with an overall accuracy of 98.2%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for online (real-time) condition monitoring in wind turbines.

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