Abstract This paper investigates the energy loss due to rotor aging for a wind turbine and its recovery. Energy loss for a wind turbine is caused by formation of dust and bugs, and corrosion and erosion of the surface of the rotor by sand and rain. We present a real-time data-driven optimization algorithm that uses dither and demodulation signals to recover the energy loss by extracting online the sensitivity of the function of interest to optimize. We use a viscid–inviscid model to represent aerodynamic performance loss of the rotor due to aging. We employ time-domain aeroservoelastic simulation of the CART3 wind turbine of the National Renewable Energy Laboratory to provide a comprehensive assessment of aging and its recovery. We also investigate the impact of aging on structural loads and levelized cost of energy. Using annual energy production and WindPACT cost models of the National Renewable Energy Laboratory, the levelized cost of energy enables an overall assessment of aging and its recovery. To evaluate the impact of the energy loss recovery on structural loading, ultimate and fatigue loads for power production design load case are used. The results of this study show 6.9% reduction in the annual energy production due to aging for a class C wind condition based on IEC61400 standard. This energy loss increases the levelized cost of energy by 7.5%. Our online optimization algorithm can recover 1.7% of the energy losses, and it results in 2.0% reduction in the levelized cost of energy. Results of the ultimate loads indicate that an aged rotor reduces structural loading on most components except the main shaft where the bending moment shows an increase of 5.3%. Rotor aging also reduces the fatigue loads in most components except the tower bottom fore-aft moment with an increase of 5.5%. The statistical inference of the results shows that the proposed optimization algorithm is effective in recovering aging related energy losses of wind turbines with a confidence level of 84% based on the sampled data in this study.

中文翻译：

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使用在线数据驱动优化技术恢复能量损失

摘要 本文研究了风力发电机转子老化引起的能量损失及其恢复。风力涡轮机的能量损失是由灰尘和虫子的形成以及沙子和雨水对转子表面的腐蚀和侵蚀造成的。我们提出了一种实时数据驱动优化算法，该算法使用抖动和解调信号通过在线提取感兴趣的函数的灵敏度进行优化来恢复能量损失。我们使用粘性-无粘性模型来表示由于老化导致的转子空气动力学性能损失。我们采用国家可再生能源实验室的 CART3 风力涡轮机的时域气动伺服弹性模拟来提供老化及其恢复的综合评估。我们还调查了老化对结构载荷和能源平准化成本的影响。使用国家可再生能源实验室的年度能源生产和 WindPACT 成本模型，能源的平准化成本能够对老化及其恢复进行全面评估。为了评估能量损失恢复对结构载荷的影响，使用了电力生产设计载荷工况的极限载荷和疲劳载荷。这项研究的结果表明，在基于 IEC61400 标准的 C 级风力条件下，由于老化导致的年发电量减少了 6.9%。这种能量损失使能源的平准化成本增加了 7.5%。我们的在线优化算法可以回收 1.7% 的能量损失，并使能量的平准化成本降低 2.0%。极限载荷的结果表明，除了主轴的弯矩增加了 5.3%，老化的转子减少了大多数部件的结构载荷。除了塔底前后力矩外，转子老化还降低了大多数部件的疲劳载荷，增加了 5.5%。结果的统计推断表明，基于本研究中的采样数据，所提出的优化算法可以有效地恢复风力涡轮机老化相关的能量损失，置信度为 84%。