针对现有风电机组功率曲线建模存在非线性拟合能力不足,且不能很好的捕捉风速与风功率之间的复杂关系,提出了一种基于数据驱动的风电机组功率曲线建模的方法(mELM-CA-LSTM).该方法利用多个极限机器学习机(Extreme Learning Machine,short for ELM)将单个的风速变量映射到多维特征空间中,组成多个特征图,通过通道注意力机制(Channel Attention,short for CA)减少高维空间特征图的冗余性,最后将长短时记忆网络(Long short-term memory network,short for LSTM)拟合风速与相应风功率之间非线性关系.对比分析了其他功率曲线建模的方法,所提的mELM-CA-LSTM方法在三个数据集上获得的最高的精度,验证了所提方法的有效性.
Data-driven Wind Turbine Power Curve Modeling Method
In view of the lack of nonlinear fitting ability of existing wind turbine power curve modeling and the inability to capture the complex relationship between wind speed and wind power,a data-driven wind turbine power curve modeling method(mELM-CA-LSTM)was proposed.This method uses multiple Extreme Learning machines(short for ELM)to map a single wind speed variable into a multidimensional feature space to form multiple feature maps.The Channel Attention mechanism(short for CA)is used to reduce the redundancy of high-dimensional spatial feature graphs.Finally,the Long short-term memory network(short for LSTM)is used to fit the nonlinear relationship between wind speed and corresponding wind power.Compared with other power curve modeling methods,the proposed mELM-CA-LSTM method obtained the highest accuracy on the three data sets,and verified the effectiveness of the proposed method.
power curve modelingdata cleaningextreme machine learning machinechannel attention mechanismlong term memory network
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