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摘要风资源具有很强的随机性和间歇性，随着大量的风电功率并网，势必会危及电力系统的安全、稳定运行，降低电能质量。对风电场输出功率进行预测，不仅可以降低风电并网对电网的冲击，提高电网运行的安全性和稳定性；还可以使电力调度部门根据风电功率的变化及时调整调度计划，使电力系统供需平衡，并可以减少系统备用容量、降低运行成本。
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本文首先根据历史数据用 BP 神经网络预测出测风塔处的风速，再进一步考虑地形因素的影响，用CFD软件对风电场风流进行数值模拟，计算出各风机轮毂高度处的风加速因数和水平偏差等数据，然后结合风速数据用 MATLAB软件编程求出各风机轮毂高度处的风速，再根据风力发电机的功率曲线算出预测功率。提出了考虑地形影响的短期风电功率预测方法。 19419
关键词 风电功率 预测 BP 神经网络 CFD软件 MATLAB
Title short-term wind power prediction methods considering
the influence of terrain
Abstract
Wind is a resource of strong random and intermittent, with a lot of wind
power joining the grid. It is bound to endanger the security and stability
of the power system. Besides, it will cause worse power quality. To predict
the power output of wind farms can not only reduce the impact of wind power
on the grid, improve security and stability of the power grid operation,
it can also help electric power dispatching departments adjust the
scheduling scheme based on wind power changes in order to keep the balance
between supply and demand Meanwhile, the system spare capacity and
operating costs are reduced.
First, this thesis predicts the wind speed with the method of BP neural
network according to historical data, and uses CFD software to simulate
the numerical operation of farm Merry when further taking the impact of
terrain into consideration. The acceleration factor and the level bias and
other data of each fan hub heigh are got .Second, combined with wind speed,
the wind speed of each fan hub heigh are calculated by MATLAB software
programming. Finally, the predicted power are estimated according to the
power curve of the wind turbine so that to propose short-term wind power
prediction methods considering the influence of terrain.
Keywords Wind power forecasting BP neural network CFD software MATLAB
目 录
1 绪论 1
1.1 课题研究的背景和意义 1
1.2 风电功率预测方法简介 1
1.3 国内外研究现状 · 3
1.4 本文的主要工作 · 4
2 风资源参数介绍 · 5
2.1 风速 5
2.2 风向 5
2.3 粗糙度 · 6
3 预测原理 7
3.1 预测方法简介 7
3.2 神经网络模型 8
3.3 CFD 软件数值模拟 9
4 BP神经网络预测风速和风向 · 11
4.1 建立预测模型 · 11
4.2 数据预测及误差分析 11
5 CFD 软件数值模拟 · 13
5.1 地形变化和地表粗糙度建模 · 13
5.2 数值模拟 · 15
5.3 模拟结果分析 · 17
6 风电场功率预测 19
6.1 预测方法 · 19
6.2 功率预测 · 19
结论 · 22
致谢 · 23
参考文献 · 24