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首页> 外文期刊>Applied System Innovation >A Feature-Based Cost Estimation Model for Wind Turbine Blade Spar Caps
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A Feature-Based Cost Estimation Model for Wind Turbine Blade Spar Caps

机译:基于特征的风电叶片翼梁盖成本估算模型

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A problem for wind turbine operators is decreasing prices for wind-generated electricity. Many turbines are approaching their rated 20-year lives. A more economically viable and sustainable solution that reduces Levelized Cost of Energy (LCOE) and avoids expensive turbine replacement is retrofitting new spar caps blades. A new cost model assesses the feasibility of retrofitting 35 to 75 m turbines with GFRP (glass fiber reinforced polymer composite) and longer length CFRP (carbon fiber reinforced composite) spar caps. Spar cap cost scales with features such as mass, volume fraction and complexity. Organizational learning is a cost factor. Material and direct labor increase as proportions of total cost while tooling, capital, utilities, and indirect labor decrease. There is good agreement between a manufacturer and the model. Twenty-year turbines were compared with retrofitted spar caps over 25 years for LCOE. Same length GFRP and longer length CFRP spar cap retrofits decrease LCOE. Longer length CFRP spar caps decrease LCOE compared with GFRP retrofits over 25 years. CFRP material cost impacts CFRP retrofit feasibility. Retrofitted turbines must meet engineering, operational performance, and planning requirements criteria. Software algorithms may improve human learning and enable automatic updates from varying design and cost inputs, thereby increasing cost prediction accuracy.
机译:风力涡轮机运营商的问题在于降低风力发电的价格。许多涡轮机的使用寿命已接近20年。降低新能源成本(LCOE)并避免昂贵的涡轮机更换的更经济可行的可持续解决方案是对新的翼梁式叶片的改造。一种新的成本模型评估了用GFRP(玻璃纤维增​​强的聚合物复合材料)和更长的CFRP(碳纤维增强的复合材料)翼梁盖改造35至75 m涡轮机的可行性。晶石帽的成本规模具有质量,体积分数和复杂性等特征。组织学习是一个成本因素。物料和直接人工随着总成本的比例而增加,而工具,资本,公用事业和间接人工则减少。制造商与模型之间有着良好的协议。对于25年的LCOE,将20年涡轮机与改装后的翼梁盖进行了比较。相同长度的GFRP和较长长度的CFRP翼梁盖改造可降低LCOE。与经过25年的GFRP改造相比,更长的CFRP翼梁盖减少了LCOE。 CFRP材料成本影响CFRP改造的可行性。改造后的涡轮机必须符合工程,运行性能和计划要求标准。软件算法可以改善人类学习能力,并能够根据变化的设计和成本输入进行自动更新,从而提高成本预测的准确性。

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