摘 要
随着全球能源结构转型的加速，风电作为重要的可再生能源，在电力系统中的渗透率持续提升。然而，风电固有的间歇性和波动性对电网稳定运行带来了显著挑战。为应对这一问题，本文聚焦于风电场集群的智能功率预测与控制研究，旨在通过先进的算法和技术手段提高风电功率预测精度，并优化风电场集群的协同控制策略。研究首先构建了基于深度学习和气象数据融合的风电功率预测模型，该模型结合长短期记忆网络（LSTM）和注意力机制，能够有效捕捉风电时间序列的非线性特征及空间相关性。其次，提出了一种多目标优化控制框架，利用强化学习方法实现风电场集群在不同工况下的最优出力分配，从而减少弃风率并增强电网适应能力。实验结果表明，所提出的预测模型相较于传统方法平均绝对误差降低约15%，而优化控制策略在典型场景下可使风电场集群的整体发电效率提升8%以上。
关键词：风电功率预测 智能控制 深度学习
Abstract
With the acceleration of global energy structure transformation, the penetration rate of wind power, as an important renewable energy, in the power system continues to increase. However, the inherent intermittence and volatility of wind power bring significant challenges to the stable operation of the power grid. In order to cope with this problem, this paper focuses on the research of intelligent power prediction and control of wind farm cluster, aiming to improve the accuracy of wind power prediction through advanced algorithms and technical means, and optimize the cooperative control strategy of wind farm cluster. Firstly, a wind power prediction model based on deep learning and meteorological data fusion is constructed. The model combining long-and short-term memory network (LSTM) and attention mechanism can effectively capture the non-linear characteristics and spatial correlation of wind power time series. Secondly, a multi-ob jective optimization control fr amework is proposed, which uses the reinforcement learning method to realize the optimal output distribution of wind farm clusters under different working conditions, so as to reduce the wind abandon rate and enhance the adaptability of the power grid. The experimental results show that the average absolute error of the proposed prediction model is reduced by about 15% compared with the traditional method, while the optimized control strategy can improve the overall power generation efficiency of the wind farm cluster by more than 8% in the typical scenario.
Keyword:Wind Power Prediction Intelligent Control Deep Learning
目 录
1绪论 1
1.1风电场集群研究的背景与意义 1
1.2智能功率预测的研究现状分析 1
1.3控制策略的技术发展综述 1
1.4本文研究方法与技术路线 2
2风电场集群功率特性建模 2
2.1风电场集群的物理特性分析 2
2.2功率输出影响因素的量化评估 3
2.3数据驱动的功率特性建模方法 3
2.4建模精度验证与优化策略 4
2.5功率特性模型的应用场景探讨 4
3智能功率预测方法研究 4
3.1预测算法的选择与比较 5
3.2基于机器学习的功率预测模型构建 5
3.3时间序列分析在预测中的应用 5
3.4不确定性因素对预测结果的影响 6
3.5提高预测精度的关键技术探讨 6
4风电场集群控制策略优化 7
4.1集群控制的基本原理与框架 7
4.2分布式控制策略的设计与实现 7
4.3实时控制算法的性能优化 8
4.4控制策略对系统稳定性的影响分析 8
4.5控制效果的实验验证与评估 9
结论 9
参考文献 11
致谢 12
