基于改进梯度提升算法的短期风电功率概率预测  

Short-term Wind Power Probability Prediction Based on Improved Gradient Boosting Machine Algorithm

作　　者：庞传军 尚学伟 张波 余建明 PANG Chuanjun;SHANG Xuewei;ZHANG Bo;YU Jianming(NARI Group Corporation(State Grid Electric Power Research Institute),Nanjing 211106,China;Beijing Kedong Power Control System Co.,Ltd.,Beijing 100192,China)

机构地区：[1]南瑞集团有限公司(国网电力科学研究院有限公司),江苏省南京市211106 [2]北京科东电力控制系统有限责任公司,北京市100192

出　　处：《电力系统自动化》2022年第16期198-206,共9页Automation of Electric Power Systems

基　　金：国家电网公司科技项目(5700-202055368A-0-0-00)。

摘　　要：随着风电大规模接入电网,风电功率概率预测的需求愈加迫切。为了实现短期风电功率概率分布预测,提出了一种基于改进梯度提升算法的短期风电功率概率预测方法。首先,分析了梯度提升算法应用于短期风电功率概率预测存在的问题。其次,利用负对数似然损失函数作为梯度提升算法中的损失函数,并利用费希尔信息矩阵修正损失函数在概率分布参数空间的梯度,将其转换为概率分布空间的自然梯度。然后,基于自然梯度提出适用于短期风电功率概率分布预测的改进梯度提升算法。最后,将所提算法与传统的梯度提升算法和其他算法进行对比,结果显示,所提算法训练过程收敛较快并且具有较好的预测性能,验证了其实用性和有效性。With the large-scale integration of wind power into the power grid,the demand for probability prediction of wind power is becoming more and more urgent.In order to realize the short-term probability distribution prediction of wind power,a short-term wind power probability prediction method based on the improved gradient boosting machine(GBM)algorithm is proposed.Firstly,the problems of the GBM algorithm applied to short-term wind power probability prediction are analyzed.Secondly,the negative log-likelihood loss function is used as the loss function in the GBM algorithm,and the Fisher information matrix is used to modify the gradient of the loss function in the parameter space of probability distribution and convert the gradient into the natural gradient of the probability distribution space.Then,based on the natural gradient,an improved GBM algorithm suitable for the short-term wind power probability distribution prediction is proposed.Finally,the proposed algorithm is compared with the traditional GBM algorithm and other methods.The results show that the training process of the proposed algorithm converges faster and has better prediction performance,which verifies the practicability and effectiveness of the proposed algorithm.

关 键 词：风电功率预测 费希尔信息 梯度提升算法 自然梯度 机器学习 

分 类 号：TM614[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]