Data-Driven Condition Monitoring Approaches to Improving Power Output of Wind Turbines

Qian, Peng; Ma, Xiandong; Zhang, Dahai; Wang, Junheng

doi:10.1109/tie.2018.2873519

Cited by 53 publications

(31 citation statements)

References 32 publications

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“…Wind turbines have witnessed a rapid growth in rating over the last 40 years [1]. However, they are usually required to operate in harsh environments, particularly offshore [2,3]. According to previous research results, gearboxes of wind turbines contribute more than 20% of failures and account for around 12 days of lost operation per annum per turbine on average [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

et al. 2019

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Effective intelligent condition monitoring, as an effective technique to enhance the reliability of wind turbines and implement cost-effective maintenance, has been the object of extensive research and development to improve defect detection from supervisory control and data acquisition (SCADA) data, relying on perspective signal processing and statistical algorithms. The development of sophisticated machine learning now allows improvements in defect detection from historic data. This paper proposes a novel condition monitoring method for wind turbines based on Long Short-Term Memory (LSTM) algorithms. LSTM algorithms have the capability of capturing long-term dependencies hidden within a sequence of measurements, which can be exploited to increase the prediction accuracy. LSTM algorithms are therefore suitable for application in many diverse fields. The residual signal obtained by comparing the predicted values from a prediction model and the actual measurements from SCADA data can be used for condition monitoring. The effectiveness of the proposed method is validated in the case study. The proposed method can increase the economic benefits and reliability of wind farms.

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Section: Introductionmentioning

confidence: 99%

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

et al. 2019

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“…In this database, there are 21 different kinds of faults, named as IDV(1), IDV(2), • • • , IDV(21) [32], [33]. IDV (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) are process faults while IDV(21) is an additional value fault. There are 22 training sets and 22 testing sets in the database, including 21 faulty data files and one normal data file.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In modern industrial systems with high degree of automation, process monitoring and fault diagnosis is one of the most important blocks, which plays an important role as it strengthens the safety as well as reliability of the industrial process. Therefore, research on fault detection methods has drawn great attention in recent years in both academia and industry [1]- [4]. Modern industrial systems have become more and more complex with the rapid development of industrial automation degree.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Fault Detection Framework Using Mahalanobis Distance Based Dynamic Time Warping

Zheng

Sun

et al. 2020

IEEE Access

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Fault detection module is one of the most important components in modern industrial systems. In this paper, we propose a novel fault detection framework which makes use of both normal and faulty measurement signals at the same time. In this framework, the multivariate time series (MTS) pieces which are extracted from measurement signals in a time interval are used as the training and testing samples, and a K-nearest neighbour rule of MTS pieces is applied for fault detection. Moreover, a Mahalanobis distance based dynamic time warping method is used to measure the divergence among MTS pieces, and a one-class metric learning algorithm is proposed to learn the appropriate Mahalanobis distance. Experimental results on the Tennessee Eastman process demonstrate that the proposed method has improved fault detection performance compared with classical approaches on certain kinds of faults.

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“…However, the cost of metal abrasive particle sensors is high and the real-time performance of oil analysis method is poor. Thus, It is necessary to further investigate low-cost solutions [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network

Luo

Liu

2020

IEEE Access

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Among the various maintenance technologies of wind turbines, online fault prediction technology is a kind of more cost-effective and reliable method. It may also be the most promising method for wind turbines with potential mechanical faults. SCADA data-based online condition monitoring technology has become a hot spot in current researches. Therefore, a novel fault prediction method based on the Pair-Copula model is proposed in this study. First, the conditional mutual information method is introduced to screen out useful variables from a number of variables. Then aiming at the limitation that the conventional Copula model can only deal with two-dimensional variables, the Pair-Copula model is introduced. In addition, the complexity of the prediction model and the dimension of the input variables are greatly reduced by the Pair-Copula model. So, the BP neural network is selected to complete the prediction model. A combined model based on BP neural network and Pair-Copula model is proposed. In order to solve the problem that the conventional Pair-Copula model cannot process real-time data which must be required in fault prediction, a kind of improved Pair-Copula model combined with the kernel density estimation is used to calculate the real-time data. Finally, the proposed method is validated with real data from a 1.5 MW wind turbine, and the effectiveness is confirmed.

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Data-Driven Condition Monitoring Approaches to Improving Power Output of Wind Turbines

Cited by 53 publications

References 32 publications

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

A Data-Driven Fault Detection Framework Using Mahalanobis Distance Based Dynamic Time Warping

A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network

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