摘 要
风力发电作为可再生能源的重要组成部分，其输出功率的随机性和间歇性对电力系统的稳定运行带来挑战。为提高风电并网的可靠性和经济性，本文聚焦于风速预测与功率控制这一关键问题，旨在建立精准的风速预测模型并提出有效的功率控制策略。研究基于多种机器学习算法构建了短期风速预测模型，通过对比分析发现长短期记忆网络（LSTM）在处理时间序列数据方面具有显著优势，能够有效捕捉风速变化规律。同时，针对风力发电机组，设计了一种自适应功率控制方法，该方法结合实时风速预测结果动态调整发电机输出功率，在保证风机安全运行的前提下最大化能量捕获效率。实验结果表明，所提出的风速预测模型预测精度较传统方法提高了约15%，功率控制策略使弃风率降低了8%左右。本研究创新性地将深度学习技术应用于风速预测，并实现了预测与控制的有机结合，为提升风力发电系统的智能化水平提供了新思路，对促进风电产业健康发展具有重要意义。
关键词：风速预测 功率控制 长短期记忆网络
Abstract
Wind power generation, as a crucial component of renewable energy, poses challenges to the stable operation of power systems due to its stochastic and intermittent output power. To enhance the reliability and economic efficiency of wind power integration, this study focuses on the critical issue of wind speed prediction and power control, aiming to establish an accurate wind speed prediction model and propose effective power control strategies. Based on multiple machine learning algorithms, a short-term wind speed prediction model was constructed, and comparative analysis revealed that Long Short-Term Memory (LSTM) networks exhibit significant advantages in handling time series data, effectively capturing wind speed variation patterns. Concurrently, an adaptive power control method for wind turbines was designed, which dynamically adjusts generator output power based on real-time wind speed predictions, maximizing energy capture efficiency while ensuring safe turbine operation. Experimental results demonstrated that the proposed wind speed prediction model achieved approximately 15% higher prediction accuracy compared to traditional methods, and the power control strategy reduced curtailment rates by around 8%. This research innovatively applies deep learning techniques to wind speed prediction and achieves an organic combination of prediction and control, providing new insights into enhancing the intelligence level of wind power generation systems and promoting the healthy development of the wind power industry.
Keyword:Wind Speed Prediction Power Control Long Short-Term Memory Network
目 录
引言 1
1风力发电系统概述 1
1.1风力发电系统构成 1
1.2风速预测的重要性 2
1.3功率控制的意义 2
2风速预测方法研究 3
2.1常规预测模型分析 3
2.2数据驱动预测技术 3
2.3混合预测模型构建 4
3功率控制策略设计 4
3.1控制目标与约束条件 4
3.2传统控制算法评估 5
3.3先进控制策略开发 5
4系统集成与优化 6
4.1预测与控制的协同 6
4.2实时性能优化方案 6
4.3系统可靠性提升措施 7
结论 8
参考文献 9
致谢 10
