基于小波变换与IAGA-BP神经网络的短期风电功率预测  

Short-term prediction of wind power based on wavelet transform andIAGA-BP neural network

作　　者：孙国良 伊力哈木·亚尔买买提[1] 张宽 吐松江·卡日[1] 李振恩 邸强 SUN Guoliang;YILIHAMU·Yaermaimaiti;ZHANG Kuan;TUSONGJIANG·Kari;LI Zhen’en;DI Qiang(The Wind Solar Storage Division of State Key Laboratory of Power System and Generation Equipment,School of Electrical Engineering,Xinjiang University,Urumqi 830049,China)

机构地区：[1]新疆大学电气工程学院电力系统及大型发电设备安全控制和仿真国家重点实验室风光储分室,乌鲁木齐830049

出　　处：《电测与仪表》2024年第5期126-134,145,共10页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(52067021);新疆维吾尔自治区高校科研计划项目(XJEDU2019Y013);新疆大学博士启动基金项目(BS190221)。

摘　　要：为提高风功率预测精度,减轻输出风能波动性对风电并网不利影响,提出了基于WT-IAGA-BP神经网络的短期风电功率预测方法。利用风速分区、3σ准则及拉格朗日插值法清洗风电场历史数据;其次,依据小波重构误差,选择db4小波分别提取风速、风向、历史风功率的不同频率特征信号,并引入改进自适应遗传算法(IAGA)对各序列BP神经网络的初始权值与阈值寻优,使用Sigmiod函数通过适应度值自适应改变交叉概率与变异概率;构建各序列的WT-IAGA-BP模型对短期风功率组合预测。通过仿真分析,并与ELM、IAGA-BP、WT-ELM及WT-LSSVM方法对比,验证该方法具有更高的预测精度和更好的预测性能。In order to improve the accuracy of wind power prediction and reduce the adverse impact of the fluctuation of output wind energy on wind power integration,a short-term wind power prediction method based on WT-IAGA-BP neural network is proposed in this paper.Firstly,data cleaning technologies,including wind speed partition,3σcriterion and Lagrange interpolation method,are applied to remove abnormal values from the historical data of wind farm.Secondly,according to the wavelet reconstruction error,db4 wavelet transform(WT)is used to extract the different frequency characteristic signals of wind speed,wind d irection and historical wind power respectively.Then,the improved adaptive genetic algorithm(IAGA)is introduced to obtain the optimized values for initial weights and thresholds of the BP neural network of each sequence,and Sigmoid function is used to adaptively change the crossover probability and mutation probability through the fitness value.Finally,the WT-IAGA-BP model of each sequence is constructed to predict the short-term wind power combination.According to the simulation analysis and comparison with other prediction models including ELM,IAGA-B,WT-ELM and WT-LSSVM,the obtained results suggest that the presented prediction model has better prediction performance and higher prediction accuracy.

关 键 词：风电功率预测 数据清洗 小波变换 改进自适应遗传算法 神经网络 

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]