EMD-based gray combined forecasting model - Application to long-term forecasting of wind power generation

Ran, Minghao; Huang, Jindi; Qian, Wuyong; Zou, Tingting; Ji, Chunyi

doi:10.1016/j.heliyon.2023.e18053

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…where κ σ (•) represents the kernel function with kernel width; E[•] is the expectation operator. The Gaussian kernel is usually used as a kernel function in (18), expressed as:…”

Section: Extended Maximum Correntropy Criterionmentioning

confidence: 99%

“…As an efficient method of data processing, modal decomposition (MD) methods [18,19] have been widely used in the field of photovoltaic and wind power prediction. This method can transform the mutation dataset into a smooth dataset in multimodal conditions, effectively reducing the impact of data mutation, but it is rarely used in the prediction of dissolved gas in transformer oil.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil

Du,

Sheng,

Liu

et al. 2024

Processes

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The prediction of dissolved gas change trends in power transformer oil is very important for the diagnosis of transformer faults and ensuring its safe operation. Considering the time series and nonlinear features of the gas change trend, this paper proposes a novel robust extreme learning machine (ELM) model combining an improved data decomposition method for gas content forecasting. Firstly, the original data with nonlinear and sudden change properties will make the forecasting model unstable, and thus an improved variational modal decomposition (IPVMD) method is developed to decompose the original data to obtain the multiple modal dataset, in which the marine predators algorithm (MPA) optimization method is utilized to optimize the free parameters of the VMD. Second, the ELM as an efficient and easily implemented tool is used as the basic model for dissolved gas forecasting. However, the traditional ELM with mean square error (MSE) criterion is sensitive to the non-Gaussian measurement noise (or outliers). In addition, considering the nonlinear non-Gaussian properties of the dissolved gas, a new learning criterion, called extended maximum correntropy criterion (ExMCC), is defined by using an extended kernel function in the correntropy framework, and the ExMCC as a learning criterion is introduced into the ELM to develop a novel robust regression model (called ExMCC-ELM) to improve the ability of ELM to process mutational data. Third, a gas-in-oil prediction scheme is proposed by using the ExMCC-ELM performed on each modal obtained by the proposed IPVMD. Finally, we conducted several simulation studies on the measured data, and the results show that the proposed method has good predictive performance.

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“…where κ σ (•) represents the kernel function with kernel width; E[•] is the expectation operator. The Gaussian kernel is usually used as a kernel function in (18), expressed as:…”

Section: Extended Maximum Correntropy Criterionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil

Du,

Sheng,

Liu

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…Renewable energy is energy harnessed from natural sources and offers the notable advantage of being inexhaustible. It includes wind power, tidal energy, geothermal energy, hydraulic power, and solar energy [ [2] , [3] , [4] , [5] ]. Solar photovoltaic (PV) system is one of the most ecologically beneficial and globally accessible form of electricity generation [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Novel MPPT algorithm based on honey bees foraging characteristics for solar power generation systems

Chen,

Farooqui,

Liu

et al. 2024

Heliyon

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“…Therefore, the “decomposition-prediction-combination reconstruction” combined model based on the combination of signal processing methods and intelligent algorithms has gradually become a research hotspot. Literature [ 23 ] proposed a short-term wind speed and wind power prediction method based on Empirical Mode Decomposition (EMD), which achieved high prediction accuracy, but the EMD method has the disadvantage of mode overlapping [ 24 ]. The Variational Mode Decomposition (VMD) proposed in the literature [ 25 ] has good nonlinear time series decomposition capability, but the effectiveness of decomposition is affected by the number of pre-set modes [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Short-term wind farm cluster power point-interval prediction based on graph spatio-temporal features and S-Stacking combined reconstruction

Hou,

Hu,

Luo

2024

Heliyon

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EMD-based gray combined forecasting model - Application to long-term forecasting of wind power generation

Cited by 8 publications

References 34 publications

A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil

A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil

Novel MPPT algorithm based on honey bees foraging characteristics for solar power generation systems

Short-term wind farm cluster power point-interval prediction based on graph spatio-temporal features and S-Stacking combined reconstruction

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