摘 要
随着全球能源需求的持续增长和对环境保护要求的不断提高，风能作为一种清洁、可再生的能源形式，在能源结构转型中占据重要地位。然而，风电场布局的合理性直接影响其发电效率与经济效益，传统优化方法在面对复杂地形、多约束条件及大规模数据处理时存在局限性。为此，本研究提出了一种基于人工智能的风电场布局优化方法，旨在通过引入机器学习算法与优化技术的深度融合，提升风电场布局设计的科学性和精确性。具体而言，研究首先构建了包含地理信息、气象数据及风机特性等多源异构数据的综合数据库，并利用深度神经网络对风速、风向等关键参数进行高精度预测。在此基础上，结合遗传算法与强化学习模型，设计了一套能够动态调整布局策略的智能优化框架，以实现风机间距、排列方向及整体分布的全局最优配置。实验结果表明，相较于传统方法，该方法能够在保证计算效率的同时显著提高风电场的年均发电量，减少能量损失并降低运维成本。此外，研究还验证了所提方法在不同地形条件下的适应性与鲁棒性，为实际工程应用提供了可靠的技术支持。本研究的主要创新点在于将人工智能技术与风电场布局优化问题有机结合，突破了传统优化方法在复杂场景中的瓶颈，同时为相关领域的进一步研究奠定了理论基础与实践参考。研究成果不仅有助于提升风电场的运行效率，也为推动可再生能源的可持续发展提供了新的思路与解决方案。
关键词：风电场布局优化；人工智能；机器学习；遗传算法；强化学习
Abstract
With the continuous growth of global energy demand and the increasing emphasis on environmental protection, wind energy, as a clean and renewable energy source, plays a crucial role in the transition of energy structures. However, the rationality of wind farm layout directly affects its power generation efficiency and economic benefits. Traditional optimization methods face limitations when dealing with complex terrains, multiple constraints, and large-scale data processing. To address these challenges, this study proposes an artificial intelligence-based optimization method for wind farm layout, aiming to enhance the scientific nature and accuracy of layout design through the deep integration of machine learning algorithms and optimization techniques. Specifically, a comprehensive database incorporating multi-source heterogeneous data, including geographical information, meteorological data, and turbine characteristics, was constructed. A deep neural network was then employed to achieve high-precision predictions of key parameters such as wind speed and wind direction. On this basis, a smart optimization fr amework capable of dynamically adjusting layout strategies was designed by combining genetic algorithms with reinforcement learning models, achieving globally optimal configurations for turbine spacing, alignment orientation, and overall distribution. Experimental results demonstrate that, compared with traditional methods, the proposed approach significantly increases the annual average power generation of wind farms while maintaining computational efficiency, reduces energy losses, and lowers operation and maintenance costs. Additionally, the study verifies the adaptability and robustness of the proposed method under various terrain conditions, providing reliable technical support for practical engineering applications. The primary innovation of this research lies in the organic combination of artificial intelligence technologies with wind farm layout optimization problems, breaking through the bottlenecks of traditional optimization methods in complex scenarios and laying a theoretical foundation and practical reference for further research in related fields. The research outcomes not only contribute to improving the operational efficiency of wind farms but also provide new insights and solutions for promoting the sustainable development of renewable energy..
Key Words：Wind Farm Layout Optimization；Artificial Intelligence；Machine Learning；Genetic Algorithm；Reinforcement Learning
目 录
摘 要 I
Abstract II
第1章 绪论 1
1.1 研究背景与意义 1
1.2 风电场布局优化的研究现状 1
1.3 本文研究方法概述 2
第2章 风电场布局优化的关键问题分析 4
2.1 风能资源特性及其对布局的影响 4
2.2 风电场选址的约束条件分析 5
2.3 传统优化方法的局限性探讨 5
2.4 人工智能技术在风电场布局中的潜力 6
第3章 基于人工智能的风电场布局优化模型构建 8
3.1 数据预处理与特征提取 8
3.1.1 风速数据采集与清洗 8
3.1.2 地形数据建模 8
3.1.3 气象参数标准化 8
3.1.4 数据质量评估方法 9
3.2 优化目标函数设计 9
3.2.1 发电量最大化目标 9
3.2.2 成本效益分析框架 10
3.2.3 环境影响最小化策略 10
3.2.4 多目标权衡机制 10
3.3 智能算法选择与改进 10
3.3.1 遗传算法的应用场景 11
3.3.2 粒子群优化的改进策略 11
3.3.3 深度学习模型的引入 11
3.3.4 混合智能算法的设计思路 12
3.4 模型验证与性能评估 12
3.4.1 测试数据集构建 12
3.4.2 评价指标体系建立 12
3.4.3 实验结果对比分析 13
3.4.4 不确定性因素考量 13
第4章 风电场布局优化的实际应用与案例研究 14
4.1 实际应用场景描述 14
4.1.1 典型风电场概况 14
4.1.2 地理环境与气象条件 14
4.1.3 技术与经济约束条件 15
4.1.4 数据获取与处理流程 15
4.2 优化方案设计与实施 15
4.2.1 初始布局方案生成 16
4.2.2 参数调优过程记录 16
4.2.3 优化结果可视化展示 16
4.2.4 方案调整与迭代改进 16
4.3 应用效果评估与分析 17
4.3.1 发电量提升效果评估 17
4.3.2 成本节约潜力分析 17
4.3.3 环境友好性验证 18
4.3.4 用户反馈与改进建议 18
4.4 案例总结与经验提炼 18
4.4.1 关键成功因素归纳 18
4.4.2 技术推广可行性分析 19
4.4.3 未来改进方向探讨 19
结 论 20
参考文献 21
致 谢 22
