Ultra-Short-Term Wind Power Prediction Based on Multivariate Phase Space Reconstruction and Multivariate Linear Regression

Liu, Rongsheng; Peng, Minfang; Xiao, Xianghui

doi:10.3390/en11102763

Cited by 29 publications

(15 citation statements)

References 31 publications

(46 reference statements)

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“…However, gathering information about the state of the site is one of the significant difficulties in realizing the physical model. Several other advanced flow modelling tools, i.e., as mesoscale weather models (MM5), the computational fluid dynamics (CFD) method was used to forecast wind variation [91,92]. These cutting-edge models can improve wind flow modelling, especially in terrain complexity.…”

Section: Numeric Weather Prediction (Nwp)mentioning

confidence: 99%

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

et al. 2020

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To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method.

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Section: Numeric Weather Prediction (Nwp)mentioning

confidence: 99%

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

et al. 2020

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“…, S tr N , a total of MN individual LSSVRs are generated. The basic LSSVR D is consisted of all the individual LSSVRs, and it is shown as the formula (11).…”

Section: B Generating the Basic Lssvrmentioning

confidence: 99%

An Online Condition Assessment of Box Substation in Wind Farm Based on Hypothesis Testing

et al. 2020

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With the rapid development of wind generation, the box substation is widely applied in the wind farm. However the box substation is more prone to break down than the conventional power transformer, due to the uncertainty of wind power. It is difficult for the existing transformer condition assessment to fulfill the requirement of ''few people on duty'' operation model, which is promoted by both the domestic and foreign wind farms. To improve the safety and reliability of the wind generation, an on-line condition assessment of the box substation in wind farm based on hypothesis testing is proposed. Firstly, an ensemble learning based on least squares support vector regression (EL-LSSVR) is proposed. Secondly, EL-LSSVR is applied to the historical data of the normal box substation, to establish a normal hot spot temperature model. Then the normal hot spot temperature model learns from the real-time data of the evaluated box substation on-line, to establish a real-time hot spot temperature model. Thirdly, the hypothesis testing is employed to analyze the significance between the real-time hot spot temperature and the normal hot spot temperature, which are generated by the real-time hot spot temperature model and the normal hot spot temperature model respectively. The condition assessment of the box substation is implemented according to the significance. Finally, the proposed method is validated by the condition assessment experiment of the box substation, which is installed at a wind farm in South China.

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“…The developed strategy is validated by simulations. Liu et al [18] study the ultra-short-term wind power prediction methods, which are based on multivariate phase-space reconstruction and linear regression. The developed method is shown to be more accurate than earlier developed methods.…”

Section: Article Papersmentioning

confidence: 99%

Power Electronics in Renewable Energy Systems

Suntio

Messo

2019

Energies

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Ultra-Short-Term Wind Power Prediction Based on Multivariate Phase Space Reconstruction and Multivariate Linear Regression

Cited by 29 publications

References 31 publications

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

An Online Condition Assessment of Box Substation in Wind Farm Based on Hypothesis Testing

Power Electronics in Renewable Energy Systems

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