基于自适应VMD样本熵动态忆阻储备池计算的风电场功率预测方法

杨宁宁; 马芝瑞; 吴朝俊

doi:doi:10.19912/j.0254-0096.tynxb.2024-1524

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基于自适应VMD样本熵动态忆阻储备池计算的风电场功率预测方法

更新时间：2026-02-06

- 基于自适应VMD样本熵动态忆阻储备池计算的风电场功率预测方法
- Vol. 47, Issue 1, Pages: 485-493(2026)
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1524
  CLC： TM615
- Online First：05 February 2026，
  
  Published：05 February 2026
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杨宁宁, 马芝瑞, 吴朝俊. 基于自适应VMD样本熵动态忆阻储备池计算的风电场功率预测方法[J]. 2026, 47(1): 485-493.
DOI：

杨宁宁, 马芝瑞, 吴朝俊. 基于自适应VMD样本熵动态忆阻储备池计算的风电场功率预测方法[J]. 2026, 47(1): 485-493. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1524.

摘要

提出一种基于变分模态分解和样本熵的动态忆阻储备池计算方法。首先

利用自适应变分模态分解技术将原始风电功率时间序列分解为一系列具有不同带宽的子模式

以降低其非线性和不稳定性。接着

通过计算各子模式的样本熵来分析其复杂度

并据此进行子模式的重组

以获得更适用于预测的子序列。最后在预测模型构建方面

引入动态忆阻储备池计算框架

结合自适应算法对储备池参数进行优化

以提高预测模型的准确性和鲁棒性。通过动态调整储备池中的连接权重和神经元状态

使模型能更好地适应风电功率的实时变化。与基于变分模态分解样本熵的反向传播神经网络、长短期记忆神经网络、动态忆阻器储备池计算相比

所提出的模型具有更高的准确性和更快的计算速度。

Abstract

With the large-scale integration of wind power into the grid

wind power forecasting is confronted with multifaceted new challenges in terms of time scales

feature capture

data processing

and uncertainty quantification. Research in this domain holds significant importance for enhancing power system stability

optimizing resource allocation

mitigating investment risks

and fostering the development of the wind power industry. This paper proposes a dynamic memristor-based reservoir computing approach that integrates variational mode decomposition and sample entropy. Initially

the adaptive VMD technique is employed to decompose the original wind power time series into a series of sub-modes with varying bandwidths

thereby reducing its nonlinearity and instability. Subsequently

the complexity of each sub-path is analyzed by calculating the sample entropy

and the sub-pah are recombined accordingly to obtain sub-sequences that are more suitable for prediction. In the aspect of prediction model construction

this paper introduces a dynamic memristor-based reservoir computing framework

which incorporates an adaptive algorithm to optimize the reservoir parameters

enhancing the accuracy and robustness of the prediction model. By dynamically adjusting the connection weights and neuron states within the reservoir

the model is better equipped to adapt to the real-time variations in wind power. When compared to the backpropagation neural network

long short-term memory neural network

and dynamic memristor-based reservoir computing based on VMD-SE

the proposed model exhibits higher accuracy and faster computation speed. In summary

the VMD-SE-DMRPC model presented in this paper demonstrates notable advantages in addressing the volatility and instability of wind power

providing a novel and effective approach for wind farm power prediction.

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