Condition monitoring of wind turbines based on analysis of temperature-related parameters in supervisory control and data acquisition data

In order to conduct a further in-depth exploration of the role of temperature-related parameters in the condition monitoring of wind turbines, this paper proposes a method to assess the condition of wind turbines by analyzing the supervisory control and data acquisition system temperature-related parameters based on existing research. A prediction model of time-sequence regression is established, based on the key temperature signals of WTs, so as to reflect their health condition in the form of prediction residuals. A kind of health index from the perspective of temperature-related parameters is developed by separating the statistics concerning the conformity of the predicted values of key temperature parameters within a certain time window from the measured values in order to clearly present the implied information on the health condition of wind turbines contained in the model prediction residuals. The case study shows that the trend of health index from the perspective of temperature-related parameters is consistent with the health condition of wind turbines. In some instances, its decline obviously occurs earlier than the maintenance provided to address the stoppage, suggesting that such indexes can effectively reflect some early health problems of the wind turbines to provide a reference for their scientific maintenance.

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Remote data acquisition for condition monitoring of wind turbines

International Journal Sustainable Construction & Design ◽

10.21825/scad.v6i1.1138 ◽

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.

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Supervisory control and data acquisition data‐based non‐linear state estimation technique for wind turbine gearbox condition monitoring

IET Renewable Power Generation ◽

10.1049/iet-rpg.2012.0215 ◽

2013 ◽

Vol 7 (4) ◽

pp. 350-358 ◽

Cited By ~ 34

Author(s):

Yue Wang ◽

David Infield

Keyword(s):

Data Acquisition ◽

Wind Turbine ◽

State Estimation ◽

Condition Monitoring ◽

Supervisory Control ◽

Estimation Technique ◽

Wind Turbine Gearbox ◽

Non Linear ◽

Linear State

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Identification of wind turbine main-shaft torsional loads from high-frequency SCADA (supervisory control and data acquisition) measurements using an inverse-problem approach

Wind Energy Science ◽

10.5194/wes-6-1401-2021 ◽

2021 ◽

Vol 6 (6) ◽

pp. 1401-1412

Author(s):

W. Dheelibun Remigius ◽

Anand Natarajan

Keyword(s):

Mathematical Model ◽

Inverse Problem ◽

Data Acquisition ◽

Wind Turbine ◽

Wind Turbines ◽

Supervisory Control ◽

High Frequency ◽

Main Shaft ◽

Site Specific ◽

Torsional Loads

Abstract. To assess the structural health and remaining useful life of wind turbines within wind farms, the site-specific structural response and modal parameters of the primary structures are required. In this regard, a novel inverse-problem-based methodology is proposed here to identify the dynamic quantities of the drivetrain main shaft, i.e. torsional displacement and coupled stiffness. As a model-based approach, an inverse problem of a mathematical model concerning the coupled-shaft torsional dynamics with high-frequency SCADA (supervisory control and data acquisition) measurements as input is solved. It involves Tikhonov regularisation to minimise the measurement noise and irregularities on the shaft torsional displacement obtained from measured rotor and generator speed. Subsequently, the regularised torsional displacement along with necessary SCADA measurements is used as an input to the mathematical model, and a model-based system identification method called the collage method is employed to estimate the coupled torsional stiffness. It is also demonstrated that the estimated shaft torsional displacement and coupled stiffness can be used to identify the site-specific main-shaft torsional loads. It is shown that the torsional loads estimated by the proposed methodology is in good agreement with the aeroelastic simulations of the Vestas V52 wind turbine. Upon successful verification, the proposed methodology is applied to the V52 turbine to identify the site-specific main-shaft torsional loads and damage-equivalent load. Since the proposed methodology does not require a design basis or additional measurement sensors, it can be directly applied to wind turbines within a wind farm that possess high-frequency SCADA measurements.

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Condition monitoring and vibration analysis of wind turbine

Advances in Mechanical Engineering ◽

10.1177/1687814020913782 ◽

2020 ◽

Vol 12 (3) ◽

pp. 168781402091378 ◽

Cited By ~ 1

Author(s):

Feng Xiao ◽

Chen Tian ◽

Isaac Wait ◽

Zhaohui (Joey) Yang ◽

Benjamin Still ◽

...

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Condition Monitoring ◽

Wind Turbines ◽

Mode Coupling ◽

Health Condition ◽

Wind Turbine System ◽

Spectral Models ◽

Non Gaussian ◽

Stochastic Properties

Health condition monitoring through comprehensive monitoring, incipient fault diagnosis, and the prediction of impending faults allows for the promotion of the long-term performance of wind turbines, particularly those in harsh environments such as cold regions. The condition monitoring of wind turbines is characterized by the difficulties associated with the lack of measured data and the nonstationary, stochastic, and complicated nature of vibration responses. This article presents a characterization of the vibrations of an operational wind turbine by spectrogram, scalogram, and bi-spectrum analyses. The results reveal varied nonstationary stochastic properties and mode-coupling instability in the vibrations of the tested wind turbine tower. The analysis illustrates that the wind turbine system vibrations exhibit certain non-Gaussian stochastic properties. An analytical model is used to evaluate the nonstationary, stochastic phenomena and mode-coupling phenomena observed in the experimental results. These results are of significance for the fault diagnosis of wind turbine system in operation as well as for improving fatigue designs beyond the wind turbulence spectral models recommended in the standards.

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Pitch fault diagnosis of wind turbines in multiple operational states using supervisory control and data acquisition data

Wind Engineering ◽

10.1177/0309524x18791407 ◽

2018 ◽

Vol 43 (5) ◽

pp. 443-458 ◽

Cited By ~ 1

Author(s):

Lu Wei ◽

Zheng Qian ◽

Cong Yang ◽

Yan Pei

Keyword(s):

Fault Diagnosis ◽

Data Acquisition ◽

Wind Turbines ◽

Supervisory Control ◽

Domain Knowledge ◽

Gaussian Mixture ◽

Abnormal Behavior ◽

Diagnosis Method ◽

Pitch System ◽

Relationship Of

Supervisory control and data acquisition data including comprehensive signal information have been widely applied to fault diagnosis. However, because of the complex operational condition of wind turbines, supervisory control and data acquisition data become complicated and abstract to study. This article proposes a pitch fault diagnosis method of wind turbines in multiple operational states using supervisory control and data acquisition data. According to the performance of characteristic parameters in nine operational states of wind turbines, Gaussian mixture model clustering and the analysis of normal performance curves are applied to model the relationship of pitch angle, rotor speed, and wind speed. Four cases have been studied to demonstrate the feasibility of the proposed method. The advantages of the proposed approach are as follows: (1) simplifying the analysis of supervisory control and data acquisition data through dividing the data into nine parts; (2) detecting pitch faults earlier than supervisory control and data acquisition monitoring system; (3) visualizing the abnormal behavior of the pitch system; and (4) improving the interpretability of the method with the incorporation of domain knowledge.

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Health condition assessment of wind turbine generators based on supervisory control and data acquisition data

IET Renewable Power Generation ◽

10.1049/iet-rpg.2018.5504 ◽

2019 ◽

Vol 13 (8) ◽

pp. 1343-1350 ◽

Cited By ~ 4

Author(s):

Junqing Li ◽

Qiujia Li ◽

Jianguo Zhu

Keyword(s):

Data Acquisition ◽

Wind Turbine ◽

Supervisory Control ◽

Condition Assessment ◽

Health Condition ◽

Turbine Generators ◽

Wind Turbine Generators

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A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data

Energies ◽

10.3390/en9110882 ◽

2016 ◽

Vol 9 (11) ◽

pp. 882 ◽

Cited By ~ 1

Author(s):

Peng Sun ◽

Jian Li ◽

Junsheng Chen ◽

Xiao Lei

Keyword(s):

Data Acquisition ◽

Wind Turbines ◽

Supervisory Control ◽

Short Term ◽

Induction Generators ◽

Doubly Fed Induction Generators ◽

Doubly Fed

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Alarms management by supervisory control and data acquisition system for wind turbines

Eksploatacja i Niezawodnosc - Maintenance and Reliability ◽

10.17531/ein.2021.1.12 ◽

2021 ◽

Vol 23 (1) ◽

pp. 110-116

Author(s):

Isaac Segovia Ramirez ◽

Behnam Mohammadi-Ivatloo ◽

Fausto Pedro García Márquez

Keyword(s):

Data Acquisition ◽

Wind Turbine ◽

Wind Turbines ◽

Supervisory Control ◽

Principal Component ◽

Data Acquisition System ◽

Fault Detection And Diagnosis ◽

Acquisition System ◽

False Alarms ◽

Original Dataset

Wind energy is one of the most relevant renewable energy. A proper wind turbine maintenance management is required to ensure continuous operation and optimized maintenance costs. Larger wind turbines are being installed and they require new monitoring systems to ensure optimization, reliability and availability. Advanced analytics are employed to analyze the data and reduce false alarms, avoiding unplanned downtimes and increasing costs. Supervisory control and data acquisition system determines the condition of the wind turbine providing large dataset with different signals and alarms. This paper presents a new approach combining statistical analysis and advanced algorithm for signal processing, fault detection and diagnosis. Principal component analysis and artificial neural networks are employed to evaluate the signals and detect the alarm activation pattern. The dataset has been reduced by 93% and the performance of the neural network is incremented by 1000% in comparison with the performance of original dataset without filtering process.

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