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.
关键词
风电场 /
功率预测 /
忆阻器 /
变分模态分解 /
时间序列
Key words
wind farms /
power forecasting /
memristors /
variational mode decomposition /
time series
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基金
国家自然科学基金（51507134）; 陕西省自然科学基础研究计划面上项目（（2021）M-449, 2018JM5068））
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