基于贝叶斯图卷积神经网络的风场内多风机风速概率预测

doi:10.19595/j.cnki.1000-6753.tces.241651

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摘要

在风机呈不规则排布的风电场中,对多风机各自风速的概率预测,有利于量化风速的不确定性与波动性,进而为风电场精细化控制提供更为丰富的信息。对此,该文提出了一种基于贝叶斯图卷积神经网络（BGCNN）的多风机风速超短期概率预测模型。为提取风机排布的空间特征信息,该模型首先以风机历史数据为依据构建了风场的初始图结构。针对固定图结构难以考虑建模误差的问题,采用BGCNN将风场图模型参数化,并通过训练其参数改变图信息,从而提取风机间空间特征。同时,在BGCNN的图卷积神经网络模块后接入长短时记忆网络,实现时间特征提取。由此,综合空间和时间相关性特征信息,得到考虑时-空二维相关性的多风机风速预测结果。最后,利用蒙特卡洛法抽样多组模型输出值,进一步得到对应概率预测结果。在呈不规则分布的多台风机上的仿真结果,验证了所提概率预测方法的有效性。

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关键词 ：概率预测, 风速预测, 图卷积神经网络, 空间相关性, 超短期预测

Abstract：

Wind speed is characterized by randomness and volatility, which makes the point forecast results cannot quantify the uncertainty of wind speed. Therefore, probabilistic wind speed forecasting is a hot topic in current wind speed forecasting research. In wind farms with large turbine intervals and irregular rows, the local wind speed of each turbine has significant differences. However, the traditional wind speed probabilistic forecasting models tend to treat the wind speeds in an area as the same value, which is not conducive to the detailed control of wind farms. To solve the above problems, this paper proposes a probabilistic wind speed forecasting model for multiple wind turbines based on Bayesian Graph Convolutional Neural Network (BGCNN).
Firstly, the original graph model of wind field was constructed based on the historical data of wind speed and wind direction of each turbine. After that, the wind field graph model was inputted into BGCNN, which can parameterize the graph model to change the graph information and extract the multi-turbine spatial correlation. In this process, the long and short-term memory network (LSTM) was plugged into the back of GCNN module in the BGCNN model, so that the feature matrix containing spatial information among turbines was inputted into the LSTM for temporal feature extraction, and the wind speed forecast results of multiple turbines considering the time-space two-dimensional correlation were obtained. Finally, the Monte Carlo method was used to sample multiple sets of model output values to achieve probabilistic wind speed forecasting for multiple turbines.
Simulation results on the wind farm data show that, multiple turbines in a wind farm present different graph structures under different epochs, indicating that the proposed model can fully consider various spatial relationships among different turbines. For point forecasting results, which are obtained by calculating the mean of the sampling results, the mean absolute error of the proposed model is 1.092m/s for all 100 wind turbines, and the root mean squared error among them is 1.530m/s. There are both smaller than the comparison model, BCNN-LSTM and Q-CNN-LSTM. For probabilistic forecasting, the reliability and sharpness of the proposed model are not all the best at different confidence level, such as in the case of 30%, 50% confidence level, etc. Nevertheless, the average forecast interval coverage bias index and average sharpness of the forecast results are 3.10% and 0.119, respectively, which are lower than the two comparison models. For turbine number 33, compared with the single turbine forecasting model, the probabilistic forecasting results of the proposed model are the best, with the smallest forecast interval coverage bias index, sharpness and Pinball score, which are 1.68%, 0.123 and 0.431m/s, respectively.
The following conclusions can be drawn from the simulation analysis: (1) In this paper, the clustering method is used to classify the output features of the GCNN-LSTM model, which enables the BGCNN to be used in regression problems and to extract the spatial features among wind turbine data well. (2) Compared to the comparison models, the model in this paper achieves higher forecast accuracy in both point forecasting and probabilistic forecasting, and the forecast intervals calculated from the forecast results have small deviations and concentrated distribution. (3) For a single wind turbine in a wind farm, the forecast accuracy of this paper's model is higher than that of the probabilistic forecasting model only for a single wind turbine, which reflects that the reasonable and effective extraction of spatial features is conducive to the reduction of forecast errors.

Key words： Probabilistic forecasting wind speed forecast graph convolutional neural network spatial correlation ultra-short-term forecasting

收稿日期: 2024-09-23

PACS:

TM614

基金资助:

国家自然科学基金资助项目（52077165）

通讯作者: 鹏男,1983年生,教授,博士生导师,研究方向为电力系统预测与控制,可再生能源并网等。E-mail：koupeng@mail.xjtu.edu.cn

作者简介: 郭美仑女,2001年生,硕士研究生,研究方向为风电概率预测、电力系统不确定性分析。E-mail：gml0317@stu.xjtu.edu.cn

引用本文:

郭美仑, 寇鹏, 田润泽, 张远航, 梁得亮. 基于贝叶斯图卷积神经网络的风场内多风机风速概率预测[J]. 电工技术学报, 0, (): 1651-. Guo Meilun, Kou Peng, Tian Runze, Zhang Yuanhang, Liang Deliang. Speed Probabilistic Forecasting of Multiple Wind Turbines in a Wind Farm Based on Bayesian Graph Convolutional Neural Network. Transactions of China Electrotechnical Society, 0, (): 1651-.

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