【摘要】 针对台风天气下海上风电出力存在秒级、分钟级剧烈波动与非线性突变而导致预测精度大幅下降的问题，提出了计及多种台风气象因素的改进粒子群算法（IPSO）-时序卷积网络（TCN）-Transformer风电功率预测模型。首先，利用改进粒子群优化算法高效完成模型关键参数寻优标定，增强模型对复杂台风气象因子的适配解析能力；其次，通过优化Transformer深度学习湍流干扰下的风电运行数据，输出高精度点预测结果，并依托门控机制分位数回归实现动态区间预测；最后，采用某海上风电实测数据完成模型验证。算例结果表明，相较于现有常规优化预测模型，所提模型适配台风极端工况能力更强，可有效削弱出力突变带来的预测偏差，兼具更高预测精度与环境鲁棒性，能够为台风场景下海上风电功率精准预测提供可靠技术支撑。更多还原

【Abstract】 To address the problem of significant prediction accuracy degradation caused by severe second-level and minute-level fluctuations and nonlinear mutations in offshore wind power output during typhoon weather, an Improved Particle Swarm Optimization(IPSO)-Temporal Convolutional Network(TCN)-Transformer wind power forecasting model is proposed, incorporating multiple typhoon meteorological factors. First, the improved particle swarm optimization algorithm is used to efficiently calibrate the key model parameters, enhancing the model′s ability to adapt and analyze complex typhoon meteorological factors. Second, the Transformer deep learning model is optimized to process wind power operational data under turbulent interference, outputting high-accuracy point prediction results, while dynamic interval prediction is achieved through a gated mechanism quantile regression approach. Finally, the model is validated using measured data from an offshore wind farm. The case study results show that, compared with existing conventional optimization prediction models, the proposed model exhibits stronger adaptability to extreme typhoon conditions,effectively mitigating prediction deviations caused by sudden output changes. It achieves both higher forecasting accuracy and environmental robustness, providing reliable technical support for accurate offshore wind power prediction under typhoon scenarios.更多还原