综合智慧能源2025，Vol.47Issue(4)：23-32,10.DOI:10.3969/j.issn.2097-0706.2025.04.002
考虑疲劳损伤的多级前馈神经网络-序列二次规划的风电场降噪优化
Noise reduction optimization of wind farms considering fatigue damage using a multi-layer feedforward neural network-sequential quadratic programming approach
摘要
Abstract
An optimization scheme was developed to address the issue of excessive stress accumulation and fatigue damage in wind turbine components caused by mismatched dispatch instructions and wind speed during actual operation.To mitigate the unavoidable interference of noise and delay inherent in long-distance sensor data transmission,a seven-level real-time noise reduction feedforward neural network was developed by combining multiple feedforward neural networks.The collected signals were first denoised before being fed into the controller for further analysis.To verify the robustness of the seven-level real-time noise reduction feedforward neural network under complex scenarios,additional noisy and delayed data were used for testing.The results confirmed that the designed noise reduction neural network met the design requirements.To achieve real-time quantification of the cumulative fatigue damage in wind turbine components,the three-point rainflow counting method was improved.The dispatch instructions for wind turbines were optimized using the Sequential Quadratic Programming(SQP)algorithm.The optimized dispatch instructions were more reasonable,leading to a more uniform distribution of cumulative fatigue damage across turbines compared to pre-optimization.This avoids the excessive accumulation of fatigue damage in individual turbines.关键词
前馈神经网络/7级实时降噪神经网络/雨流计数法/功率分配优化/SQP算法/风电场降噪优化Key words
feedforward neural network/seven-level real-time noise reduction neural network/rainflow counting method/power allocation optimization/SQP algorithm/Optimization of noise mitigation in wind farms分类
能源科技引用本文复制引用
黄琳杰,谢芷珊,廖永兴,殷林飞..考虑疲劳损伤的多级前馈神经网络-序列二次规划的风电场降噪优化[J].综合智慧能源,2025,47(4):23-32,10.基金项目
国家自然科学基金项目(62463001) National Natural Science Foundation of China(62463001) （62463001）
