Abstract

Wind energy is important to the transformation and development of global energy, because it is clean and renewable. However, the productivity of wind power is low due to its volatility, randomness, and uncertainty. Therefore, a new hybrid prediction model based on combined Elman-radial basis function (RBF) and Lorenz disturbance is proposed, which can promote the productivity of wind power by better predicting wind speed, firstly, applying the variational mode decomposition (VMD) algorithm to original nonstationary wind speed data to obtain several relatively stationary intrinsic mode functions (IMF), so as to fully exploit its potential characteristics. Meanwhile, the sample entropy is introduced to determine the decomposition number K. Afterwards, different IMF components with different characteristics are used for training and prediction: Elman neural network with sensitivity to historical state data is used for wind speed trend components; RBF with strong nonlinear mapping capability is adopted for other stochastic modal components. Next, the first-step prediction values can be obtained by reconstructing the predicted results of the respective IMF components. Finally, the Lorenz equation is introduced in view of the effects of atmospheric disturbances on wind fluctuations, which can be used to revise the first-step prediction results to obtain more realistic prediction results. By experimenting with the real data from two different wind farms and comparing with other predictive models, we found that (1) VMD can solve the problem of modal aliasing in empirical mode decomposition, to obtain a better decomposition result; (2) the combined prediction method of Elman and RBF is used for modal components, that is, different algorithms are adopted for different components, which have better prediction effects; (3) in this research, the results of the proposed combination prediction model is more accurate by comparison with the other neural network models. This research work will help the power system to rationally formulate wind farm control strategies, enhance the self-regulation of wind farm, and further promote global energy innovation.

中文翻译：

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用于预测风能资源的混合预测模型。

摘要

风能对全球能源的转化和发展至关重要，因为它是清洁和可再生的。但是，由于风能的波动性，随机性和不确定性，其生产率较低。因此，提出了一种基于Elman径向基函数（RBF）和Lorenz扰动的混合预测模型，该模型可以通过更好地预测风速来提高风力发电效率，首先将变分模式分解（VMD）算法应用于利用原始的非平稳风速数据获得多个相对平稳的固有模式函数（IMF），从而充分利用其潜在特征。同时，引入样本熵来确定分解数K。然后，将具有不同特征的不同IMF组件用于训练和预测：将对历史状态数据敏感的Elman神经网络用于风速趋势组件；具有强非线性映射能力的RBF被用于其他随机模态分量。接下来，可以通过重构各个IMF分量的预测结果来获得第一步预测值。最后，鉴于大气扰动对风场波动的影响，引入了Lorenz方程，可用于修正第一步预测结果以获得更现实的预测结果。通过试验来自两个不同风电场的真实数据并与其他预测模型进行比较，我们发现（1）VMD可以解决经验模态分解中的模态混叠问题，以获得更好的分解结果；（2）对模态分量采用Elman和RBF的组合预测方法，即对不同的分量采用不同的算法，具有较好的预测效果；（3）在本研究中，与其他神经网络模型相比，所提出的组合预测模型的结果更加准确。这项研究工作将有助于电力系统合理地制定风电场控制策略，增强风电场的自我调节能力，并进一步促进全球能源创新。具有更好的预测效果；（3）在本研究中，与其他神经网络模型相比，所提出的组合预测模型的结果更加准确。这项研究工作将有助于电力系统合理地制定风电场控制策略，增强风电场的自我调节能力，并进一步促进全球能源创新。具有更好的预测效果；（3）在本研究中，与其他神经网络模型相比，所提出的组合预测模型的结果更加准确。这项研究工作将有助于电力系统合理地制定风电场控制策略，增强风电场的自我调节能力，并进一步促进全球能源创新。