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| 风电叶片单轴疲劳试验弯矩匹配智能优化 |
| Moment Matching Optimization Method for Single Point Fatigue Test of Wind Power Blades |
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| DOI：10.16450/j.cnki.issn.1004-6801.2022.03.011 |
| 中文关键词: 风力叶片 疲劳测试 弯矩匹配 混合粒子群算法 |
| 英文关键词:wind turbine blade fatigue test bending moment matching hybrid particle swarm algorithm |
| 基金项目:国家重点研发计划资助项目（2018YFB1501203）；山东省自然科学基金资助项目（ZR2021ME160） |
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| 中文摘要: |
| 为了使风电叶片疲劳试验中的试验弯矩与目标弯矩匹配，进而准确获得叶片疲劳特性，提出了采用改进的智能优化算法进行等效配重块布置的智能优化方案。通过模态试验参数辨识确定旋转质量块激振频率应等于叶片一阶固有频率，引入叶片自重作用弯矩分量并构建截面弯矩计算模型。基于差分进化变异的混合粒子群优化算法，以均方误差为适应度函数进行弯矩分布和幅值控制问题联合优化。采用LZ40.3-1.5叶片进行优化技术应用，得出疲劳试验弯矩分布的主要影响因素为激振装置及配重块个数、质量及位置，所设计的算法将关键截面弯矩误差控制在7%以内，验证了单轴疲劳试验弯矩匹配的配重优化方案的正确性及可行性。 |
| 英文摘要: |
| In order to match the test bending moment with the target bending moment in the fatigue test of wind turbine blade， an intelligent optimization scheme that uses an improved intelligent optimization algorithm for the arrangement of equivalent weights is proposed to accurately obtain the blade's fatigue characteristics. Through the identification of modal test parameters， the excitation frequency of the rotating mass equaling to the first-order natural frequency of the blade is determined， and a section bending moment calculation model is constructed to introduce the bending moment component of the blade's weight. Based on the hybrid particle swarm optimization algorithm introduced differential evolution mutation， the jointly optimization of bending moment distribution and amplitude control problems is performed using the mean square error as the fitness function. Using the LZ40.3-1.5 blade for optimization， it is concluded that the main influencing factors of the bending moment distribution of fatigue test are the number， quality and position of the vibration excitation device and counterweight. The bending moment's errors at the key section are controlled within 7% by the designed algorithm， verifying the correctness and feasibility of the counterweight optimization scheme of bending moment matching in the uniaxial fatigue test. |
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