研究一种基于混合神经网络的风电场测风数据插补模型,该模型（CNN-LSTM-SA）的超参数通过PSO-GWO优化算法优化,然后对测风数据进行插补。首先选取待插补高度下的两个相邻高度的测风数据、中尺度数据及待插补高度其他时间段的风速数据,建立一个“3种特征1个目标数据”的回归模型,然后使用该模型对其目标插补数据进行预测以达到插补的目的。以辽宁某风电场的测风数据进行仿真验证,仿真结果表明,该方法归一化均方误差NMSE为0.0021、发电量为1143732 kWh,均优于工程中常用方法的插补结果,对工程实际工作具有一定的参考意义。
Abstract
This paper presents a wind measurement data interpolation model for wind farms based on a hybrid neural network, the hyperparameters of this model （CNN-LSTM-SA） are optimized using the PSO-GWO optimization algorithm, and then the wind measurement data is interpolated. First, the wind measurement data at two adjacent heights under the height to be interpolated, the mesoscale data, and the wind speed data at other time periods at the height to be interpolated are selected to establish a regression model with "three features and one target data." Then, the target interpolated data is predicted using this model to achieve the purpose of interpolation. In this paper, the wind measurement data of a certain wind farm in Liaoning is used for simulation verification. The simulation results show that the method has an NMSE error of 0.0021 and a power generation of 1143732 kWh, which is both superior to the interpolation results of commonly used methods in engineering. It has certain reference significance for the practical work of the project.
关键词
风电场 /
风资源评估 /
插补 /
神经网络 /
优化算法 /
超参数
Key words
wind farm /
wind resource assessment /
interpolation /
neural network /
optimization algorithm /
hyperparameters
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基金
辽宁省自然科学基金联合基金面上项目（项目编号2023-MSLH-263）
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