风力发电机输出功率条件子集区间估计方法

根据风力发电机的风速和输出功率历史数据,对风机输出功率均值与风速的关系进行最小二乘参数辨识,以此为基础求出风机输出功率偏差与风速的关系。采用Python概率分析确定风机输出功率偏差各子集的概率分布类型,估计其特征参数,以求出其概率密度函数,进而对风机输出功率偏差进行概率置信区间预测。基于风机输出功率均值与风速的关系和风机输出功率偏差子集的置信区间估计模型,实现了根据风速预报值对风机输出功率的置信区间进行预测。用实际风机的历史纪录对所提方法进行了测试和验证。结果表明:基于风速将风机输出功率偏差划分成多个子集,可提高风机输出功率概率置信区间预测的精准度。

Conditional Subset Interval Estimation Method forWind Turbine Output Power

According to the wind speed and output power historical data of wind turbine, the relationship between the average output power of the wind turbine and the wind speed was subjected to least squares parameter identification, and the relationship between the output power deviation of the wind turbine and the wind speed was obtained based on the above research. The Python probabilistic analysis was used to determine the probability distribution types of each subset of the wind turbine output power deviation, and the characteristic parameters were estimated to obtain the probability density function, furthermore, the probability of confidence interval prediction was performed on the wind turbine output power deviation. Based on the relationship between the average output of the wind turbine and the wind speed and the confidence interval estimation model of the wind turbine output power deviation subset, the confidence interval of the wind turbine output power was predicted according to the wind speed forecast value. The method proposed was tested and verified with the historical record of the actual wind turbine, the confidence interval of the wind turbine output power was predicted according to the wind speed forecast value. The results showed that the wind turbine output power deviation was divided into multiple subsets based on the wind speed, which could improve the accuracy of the confidence interval prediction of the wind turbine output power.