基于CFD流场预计算的复杂地形风电场功率预测方法研究
发布时间：2019-03-27 08:46
【摘要】：由于风能具有随机波动性,大规模风电并网给电力系统的安全和经济运行带来不利影响,而准确的风电场功率预测是缓解上述问题的有效途径。基于CFD流场预计算的风电场功率预测方法一方面可以针对复杂地形有目的性地细化计算模型,准确模拟风电场的空气流动特征,通过精细的流场计算提高预测精度；另一方面将流场计算放在功率预测之前进行,大幅提高模型的计算效率。该方法无需历史数据训练建模,适用于历史数据不足、地形复杂、气候多变的风电场。 论文主要内容和研究结果包括： (1)针对复杂地形风电场建立了功率预测的CFD模型及预计算数据库 研究了平坦地形风电场功率预测CFD方法的预测误差特点,通过分析风速、风向对预测误差的影响规律,提出了基于风况离散的预计算数据库优化方法,局部细化CFD模型和预计算数据库,在准确模拟风电场流场特征的同时,尽可能提高模型的计算效率。 (2)建立了基于预计算方法的风电场功率预测模型 预计算方法是根据流场特征建立预计算数据库,预存储风电场CFD模型的计算结果,在实际运行中根据实际风况调用数据库中的相应功率预测结果,实现风电场功率预测。以中国西北部的某复杂地形风电场为例验证模型精度,结果表明,预计算方法在输出功率短时间内由满发降至零出力、由零出力增至满发以及大幅波动这三种对电网冲击最大的风电场出力变化状态下表现出良好的跟踪能力。 (3)建立了三种数值天气预报风速修正模型 基于最小二乘、支持向量机、径向基神经网络原理建立了三种数值天气预报风速的修正模型,以提高数值天气预报和整体功率预测的精度；分别以实测、原始NWP及三种修正NWP风速为模型输入,研究了不同修正方法的误差修正效果,并分析了模型输入误差对基于CFD流场预计算的功率预测精度的影响。
[Abstract]:Due to the random fluctuation of wind power, the large-scale wind power grid connection brings adverse effects on the security and economic operation of the power system, and accurate wind farm power prediction is an effective way to alleviate the above-mentioned problems. On the one hand, the wind farm power prediction method based on the precomputation of CFD flow field can, on the one hand, refine the calculation model for complex terrain, accurately simulate the air flow characteristics of wind farm, and improve the prediction precision by fine calculation of flow field. On the other hand, the calculation of flow field is carried out before the power prediction, which greatly improves the computational efficiency of the model. This method does not require historical data training and modeling, and is suitable for wind farms with insufficient historical data, complex topography and changeable climate. The main contents and results of this paper are as follows: (1) the CFD model of power prediction for complex terrain wind farm is established and the prediction error characteristics of the CFD method for power prediction of flat terrain wind farm are studied, and the prediction error characteristics of CFD method for power prediction of flat terrain wind farm are studied. Based on the analysis of the influence of wind speed and wind direction on the prediction error, the optimization method of precomputation database based on wind condition discretization is put forward. The CFD model and precomputation database are refined locally, and the flow field characteristics of wind farm are simulated accurately at the same time. The computational efficiency of the model is improved as much as possible. (2) the pre-calculation method of wind farm power prediction model based on precomputation method is to establish the precomputation database according to the characteristics of the flow field, and store the calculation results of the CFD model of wind storage farm. In the actual operation, the corresponding power prediction results in the database are called according to the actual wind conditions, and the wind farm power prediction is realized. A complex terrain wind farm in northwest China is taken as an example to verify the accuracy of the model. The results show that the precomputation method decreases from full output to zero output in a short time. The three wind farms, which have the biggest impact on the power grid, show good tracking ability under the condition of zero output increasing to full generation and large fluctuation. (3) based on the principle of least square, support vector machine and radial basis function neural network, three kinds of modified models of numerical weather forecast wind speed are established, which are based on the principle of least square, support vector machine and radial basis function neural network. In order to improve the accuracy of numerical weather forecast and overall power prediction; Taking the measured, original NWP and three modified NWP wind speeds as the model inputs, the error correction effects of different correction methods are studied, and the influence of the model input errors on the power prediction accuracy based on the CFD flow field prediction is analyzed.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM614
本文编号：2448044
[Abstract]:Due to the random fluctuation of wind power, the large-scale wind power grid connection brings adverse effects on the security and economic operation of the power system, and accurate wind farm power prediction is an effective way to alleviate the above-mentioned problems. On the one hand, the wind farm power prediction method based on the precomputation of CFD flow field can, on the one hand, refine the calculation model for complex terrain, accurately simulate the air flow characteristics of wind farm, and improve the prediction precision by fine calculation of flow field. On the other hand, the calculation of flow field is carried out before the power prediction, which greatly improves the computational efficiency of the model. This method does not require historical data training and modeling, and is suitable for wind farms with insufficient historical data, complex topography and changeable climate. The main contents and results of this paper are as follows: (1) the CFD model of power prediction for complex terrain wind farm is established and the prediction error characteristics of the CFD method for power prediction of flat terrain wind farm are studied, and the prediction error characteristics of CFD method for power prediction of flat terrain wind farm are studied. Based on the analysis of the influence of wind speed and wind direction on the prediction error, the optimization method of precomputation database based on wind condition discretization is put forward. The CFD model and precomputation database are refined locally, and the flow field characteristics of wind farm are simulated accurately at the same time. The computational efficiency of the model is improved as much as possible. (2) the pre-calculation method of wind farm power prediction model based on precomputation method is to establish the precomputation database according to the characteristics of the flow field, and store the calculation results of the CFD model of wind storage farm. In the actual operation, the corresponding power prediction results in the database are called according to the actual wind conditions, and the wind farm power prediction is realized. A complex terrain wind farm in northwest China is taken as an example to verify the accuracy of the model. The results show that the precomputation method decreases from full output to zero output in a short time. The three wind farms, which have the biggest impact on the power grid, show good tracking ability under the condition of zero output increasing to full generation and large fluctuation. (3) based on the principle of least square, support vector machine and radial basis function neural network, three kinds of modified models of numerical weather forecast wind speed are established, which are based on the principle of least square, support vector machine and radial basis function neural network. In order to improve the accuracy of numerical weather forecast and overall power prediction; Taking the measured, original NWP and three modified NWP wind speeds as the model inputs, the error correction effects of different correction methods are studied, and the influence of the model input errors on the power prediction accuracy based on the CFD flow field prediction is analyzed.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM614
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