基因最小平方估计方法用于风力发电曲线建模和风力发电预测
Zhiming Wang1, Xuan Wang2, Weimin Liu2
1School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou, 730050, China. wangzhiming301@sohu.com.
Scientific reports
|June 6, 2023
概括
一种新的遗传最小平方估计 (GLSE) 方法提高了风力轮机功率曲线建模和预测准确度. 这种方法有效地估计了参数,避免了可靠风力发电预测的局部最佳情况.
科学领域:
- 可再生能源工程可再生能源工程
- 计算智能是一种计算智能.
- 统计建模 统计建模
背景情况:
- 风力发电曲线 (WPC) 对于风力轮机性能分析,预测和监测至关重要.
- 准确的WPC建模在参数估计,初始值选择和避免局部最佳值方面面临挑战.
研究的目的:
- 为风力轮机功率曲线建模和参数估计开发一种可靠的方法.
- 提高风力发电预测和条件监测的准确性.
- 为了比较不同功率曲线模型的性能.
主要方法:
- 提出了一种遗传最小平方估计 (GLSE) 方法,将遗传算法和最小平方估计相结合,用于参数估计.
- 使用六个评估指数 (RMSE,R2,MAE,MAPE,AICc,BIC) 进行最佳的模型选择,并防止过度装配.
- 应用了用于风速建模的二元韦布尔混合物分布和用于功率曲线模型的五参数物流函数.
主要成果:
- 通过GLSE方法实现了WPC参数的全球最佳估计结果.
- 提出的方法在WPC建模和风力发电预测方面证明了可行性和有效性.
- 与多项式和四参数后勤模型相比,五参数后勤函数在准确度相似的情况下显示出更好的合适精度.
结论:
- 采用GLSE方法显著提高了风力轮机功率曲线参数估计的准确性.
- 使用GLSE方法精确的WPC建模可以提高风力发电预测和轮机状况监测.
- 五参数物流函数是WPC的合适模型,特别是与其他常见模型相比.
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