Abstract:

In order to enable the wind power system run smoothly and reduce the economic loss caused by power system fluctuation, and at the same time improve the competitiveness of the wind power system, it is of great and practical significance to find a stable and accurate wind speed prediction method. Among the machine learning methods, a neural network based on the back propagation (BP) algorithm to adjust the weight is one of the most commonly used and most effective methods. Although the BP neural network has a strong ability to fit nonlinearity, it converges slowly in the process of adjusting weight and easily falls into local optimal values. In order to effectively solve these two problems that will occur in the prediction process of the BP neural network, genetic algorithm (GA) was used to optimize the neural network in this paper. On this basis, considering the intermittency, non?stationarity and difference of wind speed series, a short?term wind speed prediction model EMD?GA?BPNN based on empirical mode decomposition (EMD), genetic algorithm (GA) and BP neural network was proposed. The reliability and advantages of this model in short?term wind speed prediction were verified by transverse comparisons with other models.

Key words: Wind speed prediction, Neural network, Signal decomposition, Empirical mode decomposition

摘要：

为了让风电电力系统在并网时能够平稳运行，降低因系统波动带来的经济损失，同时提高风电电力系统的竞争能力，找到一种稳定准确的风速预测方法有着重要且现实的意义。在机器学习的方法中，基于反向传播算法调整权值的BP神经网络是最常用也是最有效的方法之一。尽管BP神经网络拟合非线性序列的能力很强，但是在调整权值的过程中收敛速度慢，同时十分容易陷入局部最优值，为有效解决这两个可能出现的问题，将遗传算法(GA)用于优化神经网络。在此基础上，考虑到风速序列的间歇性、非平稳性以及差异性等特点，提出了一种基于经验模态分解(EMD)、遗传算法(GA)和BP神经网络的短期风速预测模型EMD?GA?BPNN，通过和其他几种模型的横向对比，验证了此模型在短期风速预测效果上的可靠性与优势。

关键词: 风速预测, 神经网络, 信号分解, 经验模态分解