Fault diagnosis and condition monitoring of wind turbines

Niemann, Hans Henrik; Poulsen, Niels Kjølstad; Mirzaei, Mahmood; Henriksen, Lars Christian

doi:10.1002/acs.2782

Cited by 19 publications

(8 citation statements)

References 26 publications

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“…Furthermore, in this case, a trial and error procedure was used to determine the optimal number of delays d u and d y , as well as the number of neurons, that led to the minimization of the fault estimation error. In particular, d u = d y = 4 delays were selected in the relation of Equation (9). According to Table 3 and Figure 3, the NN models have three inputs.…”

Section: Faultmentioning

confidence: 99%

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Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

Simani

Castaldi

2019

Applied Sciences

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Fault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines.

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Section: Faultmentioning

confidence: 99%

“…Accordingly, the number of studies and consequent publications has increased considerably, and the subject is intensively researched worldwide [8]. However, there are only a few available review papers in this field [7,9].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

Simani

Castaldi

2019

Applied Sciences

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“…The wavelet packet energy is employed as a frequency feature. The expression of wavelet packet energy [29] is written in Equation (11).…”

Section: The Concept Of Esnmentioning

confidence: 99%

“…For instance, condition monitoring of machine-bearing housing using the measured experimental values of the acceleration amplitude of mechanical vibrations was studied in Reference [8], a new switching unscented Kalman filter algorithm was proposed in Reference [9] to predict the remaining useful life of bearings, and a method based on a generalized synchro-squeezing transform with vibration data was proposed to detect gearbox faults in Reference [10]. In addition, work in condition monitoring for machinery includes an approach using moment and rotor angle sensors that was proposed to monitor wind turbines [11] and the use of multiple sparse auto-encoders reported for dynamic state monitoring of 3D printers [12]. At present, new intelligent classification methods such as artificial neural networks (ANNs) [13], SVM [14], fuzzy logic [15], and neighborhood preserving embedding (NPE) [16] have been gradually employed for condition monitoring.…”

Section: Introductionmentioning

confidence: 99%

Transmission Condition Monitoring of 3D Printers Based on the Echo State Network

Zhang

Cabrera

et al. 2019

Applied Sciences

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Three-dimensional printing quality is critically affected by the transmission condition of 3D printers. A low-cost technique based on the echo state network (ESN) is proposed for transmission condition monitoring of 3D printers. A low-cost attitude sensor installed on a 3D printer was first employed to collect transmission condition monitoring data. To solve the high-dimensional problem of attitude data, feature extraction approaches were subsequently performed. Based on the extracted features, the ESN was finally employed to monitor transmission faults of the 3D printer. Experimental results showed that the fault recognition accuracy of the 3D printer was obtained at 97.17% using the proposed approach. In addition, support vector machine (SVM), locality preserving projection support vector machine (LPPSVM), and principal component analysis support vector machine (PCASVM) were also used for comparison. The contrast results showed that the recognition accuracies of our method were higher and more stable than that of SVM, LPPSVM, and PCASVM when collecting raw data via the low-cost attitude sensor.

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“…It can promptly remind wind farm operators to pay attention and develop corresponding condition-based maintenance plans in advance. Furthermore, monitoring the running states of all the wind turbines in wind fields can reduce the maintenance cost and the probability of sudden outage (Niemann et al, 2018). This in turn translates into improved safety and production efficiency of the wind field (Amirat et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Online health assessment of wind turbine based on operational condition recognition

Zhang

Jiang

et al. 2018

Transactions of the Institute of Measurement and Control

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To reduce the operation and maintenance (O&M) costs, the health assessment of wind turbine has received more and more attention in recent years. However, it is difficult to evaluate the health condition of wind turbine due to the complex and non-stationary operation environment. This paper proposes a data-driven approach for online health assessment of wind turbine based on operational condition recognition. First, the operational condition parameters are selected by analyzing the monitoring data of wind turbine. Considering the time-varying of operation environment, the operational conditions are divided into four subspaces utilizing the K-means clustering algorithm. Then, using the historical state parameters data under normal operation, a health benchmark model is constructed in each operational condition space based on Gaussian Mixture Model (GMM). Further, a Softmax model is trained according to the results of operational condition classification, which is used to identify the online operational condition of wind turbine. Moreover, an overall health index (HI) based on Mahalanobis distance is developed to assess the health condition of wind turbine. Finally, the method is verified by the actual supervisory control and data acquisition (SCADA) data of a wind field in northwestern China. The test results show that the proposed approach can track the running state of the wind turbine accurately and play a good role in early fault warning.

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Fault diagnosis and condition monitoring of wind turbines

Cited by 19 publications

References 26 publications

Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

Transmission Condition Monitoring of 3D Printers Based on the Echo State Network

Online health assessment of wind turbine based on operational condition recognition

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