A novel multi-information fusion grey model and its application in wear trend prediction of wind turbines

Yang, Xiaoyu; Fang, Zhigeng; Yang, Yingjie; David, Éric; Li, Xiaochuan

doi:10.1016/j.apm.2019.02.043

Cited by 34 publications

(14 citation statements)

References 12 publications

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“…We compared our method with the traditional gray model to show the superiority of using EB on gray model. Gray model is often compared with mathematical models, such as linear regression, exponential regression, and AR models 20 . AR models are one of the most popular linear models in time series forecasting, which have been widely applied during the past decade.…”

Section: Results and Discusstionmentioning

confidence: 99%

Remaining service life prediction based on gray model and empirical Bayesian with applications to compressors and pumps

David

Okoroigwe

et al. 2020

Quality & Reliability Eng

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In this study, a three‐step remaining service life (RSL) prediction method, which involves feature extraction, feature selection, and fusion and prognostics, is proposed for large‐scale rotating machinery in the presence of scarce failure data. In the feature extraction step, eight time‐domain degradation features are extracted from the faulty variables. A fitness function as a weighted linear combination of the monotonicity, robustness, correlation, and trendability metrics is defined and used to evaluate the suitability of the features for RSL prediction. The selected features are merged using a canonical variate residuals‐based method. In the prognostic step, gray model is used in combination with empirical Bayesian algorithm for RSL prediction in the presence of scarce failure data. The proposed approach is validated on failure data collected from an operational industrial centrifugal pump and a compressor.

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Section: Results and Discusstionmentioning

confidence: 99%

Remaining service life prediction based on gray model and empirical Bayesian with applications to compressors and pumps

David

Okoroigwe

et al. 2020

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

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“…Ma et al defined a new information priority accumulated grey model with time power to predict short-term wind turbine capacity [6]. Yang et al put forward a method to enhance the prediction accuracy and robustness of the grey prediction model by introducing multisource information into traditional grey models [7]. Wu and Cong propose a novel method for short-term wind power forecasting, which combines the wavelet transform, particle swarm optimization dynamic grey model, and Lyapunov exponent prediction method [8].…”

Section: Grey Prediction Model In Wind Power In Wind Powermentioning

confidence: 99%

Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

Luo

Zeng

et al. 2021

Journal of Mathematics

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The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly, considering the modeling mechanism and process, the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly, as different orders of accumulation generation correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%, which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%, respectively). The forecast shows that China’s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments.

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“…The grey system theory is applicable to the study of systems with small samples and poor information. By means of the development and analysis of partial information, useful information can be extracted, and the behavior and evolution of the system can be predicted [41].…”

Section: B Grey System Prediction Modelmentioning

confidence: 99%

Wind Power Prediction Based on PSO-SVR and Grey Combination Model

2019

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As a kind of green, clean and renewable energy, wind power generation has been widely utilized in various countries in the world. With the rapid development of wind energy, it is also facing prominent problems. Because wind power generation is intermittent, unstable and stochastic, it has caused serious difficulties for power grid dispatch. At present, the important method to solve this problem is to predict wind speed and wind power. Grey model is suitable for uncertain systems with poor information and needs less operation data, so it can be used for wind speed and wind power prediction. However, the traditional grey system model has the disadvantage of low prediction accuracy. Therefore, firstly the GM (1,1) for wind speed prediction is improved by background value optimization in this paper. In order to comprehensively reveal the inherent uncertainty of wind speed random series, the fractional order grey system models with different orders are constructed. Secondly, in order to overcome the shortcoming of single grey model, each grey model is effectively united, and a combination prediction model based on neural network is presented. The two NWP outputs, i.e. ECMWF and GRAPES-MESO, have been added to the prediction model for reducing the uncertainty. The structure parameters of the neural network are optimized by trial and error. Thirdly, the support vector regression model is established to fit the scatter operation data of wind speedpower, and the parameters of the model are optimized by the particle swarm algorithm. Then the power prediction value is obtained by the fitted wind speed-power relationship and the corresponding result of the grey combination model for wind speed prediction. Finally, wind speed and wind power are predicted based on the actual operation data. In addition, the prediction model based on ARIMA is also constructed as a benchmark model. The results show that the proposed grey combination model improves the prediction accuracy.INDEX TERMS Grey model, background value, support vector regression, fractional order, particle swarm algorithm, wind speed prediction, wind power prediction, combination model.

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A novel multi-information fusion grey model and its application in wear trend prediction of wind turbines

Cited by 34 publications

References 12 publications

Remaining service life prediction based on gray model and empirical Bayesian with applications to compressors and pumps

Remaining service life prediction based on gray model and empirical Bayesian with applications to compressors and pumps

Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

Wind Power Prediction Based on PSO-SVR and Grey Combination Model

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