Online health assessment of wind turbine based on operational condition recognition

Zhang, Jing; Jiang, Na; Li, Huike; Li, Ning

doi:10.1177/0142331218810070

Cited by 36 publications

(28 citation statements)

References 37 publications

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“…This kind of approaches has been recently developing in the wind energy literature for performance monitoring and condition monitoring purposes. For some examples, refer to [27][28][29][30]. For a comprehensive point of view about the use of operation data in wind energy and about data-driven power curve models, refer to [31].…”

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confidence: 99%

A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis

et al. 2020

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Ageing of technical systems and machines is a matter of fact. It therefore does not come as a surprise that an energy conversion system such as a wind turbine, which in particular operates under non-stationary conditions, is subjected to performance decline with age. The present study presents an analysis of the performance deterioration with age of a Vestas V52 wind turbine, installed in 2005 at the Dundalk Institute of Technology campus in Ireland. The wind turbine has operated from October 2005 to October 2018 with its original gearbox, that has subsequently been replaced in 2019. Therefore, a key point of the present study is that operation data spanning over thirteen years have been analysed for estimating how the performance degrades in time. To this end, one of the most innovative approaches for wind turbine performance control and monitoring has been employed: a multivariate Support Vector Regression with Gaussian Kernel, whose target is the power output of the wind turbine. Once the model has been trained with a reference data set, the performance degradation is assessed by studying how the residuals between model estimates and measurements evolve. Furthermore, a power curve analysis through the binning method has been performed to estimate the Annual Energy Production variations and suggests that the most convenient strategy for the test case wind turbine (running the gearbox until its end of life) has indeed been adopted. Summarizing, the main results of the present study are as follows: over a ten-year period, the performance of the wind turbine has declined of the order of 5%; the performance deterioration seems to be nonlinear as years pass by; after the gearbox replacement, a fraction of performance deterioration has been recovered, though not all because the rest of the turbine system has been operating for thirteen years from its original state. Finally, it should be noted that the estimate of performance decline is basically consistent with the few results available in the literature.

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confidence: 99%

A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis

et al. 2020

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“…Thus, the Gaussian mixture model (GMM) is selected when describing the distribution of residual set. The GMM model is a linear combination of multiple single gaussian models (SGM) [23]. All data can be considered to be generated by multiple SGM, and the linear combination of SGM can be infinitely close to the real data distribution.…”

Section: B Gaussian Mixture Modementioning

confidence: 99%

“…After the residual extraction model is built, the standant residual set of each state is fitted accurately based on GMM. Combined with the GMM benchmark model of each running state, the mahalanobis distance is adopted to achieve health index measurement [23]. Health assessment is conducted per hour.…”

Section: Health Measurement Indexmentioning

confidence: 99%

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Multi-Running State Health Assessment of Wind Turbines Drive System Based on BiLSTM and GMM

et al. 2020

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With the continuous elevation of demand for large-scale wind turbines and operation & maintenance cost an increasing interest has been rapidly generated on CM (Condition Monitoring) system. The main components of wind turbines are the focus on all CM as they overall lead to high repair costs and equipment downtime. Thus, it is difficult to make comprehensive assessment in the assessment. In the present study, intelligent machine learning algorithms are adopted to mine SCADA (Supervisory Control and Data Acquisition) system data of WTs (wind turbines). Besides, based on bidirectional long short-term memory (BiLSTM) neural networks and gaussian mixture model (GMM) algorithm, this study developed a multi-running state health assessment model for the drive system of wind turbines. First, the stateidentification model is built with health data to overcome the effect of the time-varying characteristics of running environment and alterations of running condition during the assessment. Then, in each state, the BiLSTM algorithm is adopted to extract the residual set of valid state variables, and the GMM algorithm is employed to accurately fit the distribution of residual set. The multi-running state benchmark model based on BiLSTM and GMM is built. Subsequently, the drive system of wind turbines health degree is calculated by health index. Lastly, based on multiple driving system faults data of a wind turbine, the feasibility and validity of the model are verified.

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“…Typically, a CBM system includes seven parts: sensor, signal processing system, fault detection system, health assessment system, fault prediction system, test support system, and finally display part, where failure prediction is a core part. Recently, more and more attention of academy has been focused on failure prediction methods, as well as the industrial field [2]. And, numerous failure prediction methods have been proposed, which can be divided into two categories: model-based methods and datadriven methods [3].…”

Section: Introductionmentioning

confidence: 99%

Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation

Yang

Yao

et al. 2019

Int J Parallel Prog

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Remaining useful life (RUL) prediction plays an important role in guaranteeing safe operation and reducing maintenance cost in modern industry. In this paper, we present a novel deep learning method for RUL estimation based on time empirical mode decomposition (EMD) and temporal convolutional networks (TCN). The proposed framework can effectively reveal the non-stationary characteristics of bearing degradation signals and acquire time-series degradation signals which namely intrinsic mode functions through empirical mode decomposition. Furthermore, the feature information is used as the input to convolution layer and trained by TCN to predict remaining useful life. The proposed EMD-TCN model structure maintains a superior result compared to several state-of-the-art convolutional algorithms on public data sets. Experimental results show that the average score of EMD-TCN model is improved by 10-20% than traditional convolutional algorithms.

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Online health assessment of wind turbine based on operational condition recognition

Cited by 36 publications

References 37 publications

A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis

A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis

Multi-Running State Health Assessment of Wind Turbines Drive System Based on BiLSTM and GMM

Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation

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