基于小波变换与IAGA-BP神经网络的短期风电功率预测

孙国良; 伊力哈木.亚尔买买提; 张宽; 吐松江.卡日; 李振恩; 邸强

孙国良,伊力哈木.亚尔买买提,张宽,吐松江.卡日,李振恩,邸强.基于小波变换与IAGA-BP神经网络的短期风电功率预测[J].电测与仪表,2024,61(5):126-134.
Sun Guoliang,Yilihamu Yaermaimaiti,Zhang Kuan,Tusongjiang.Kari,Li Zhen-En,Di Qiang.Short-term prediction of wind power based on wavelet transform and IAGA-BP neural network[J].Electrical Measurement & Instrumentation,2024,61(5):126-134.

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 and IAGA-BP neural network

DOI：10.19753/j.issn1001-1390.2024.05.018

中文关键词: 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

英文关键词:wind power prediction, data cleaning, wavelet transform, improved adaptive genetic algorithm, neural network

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

作者中文名	作者英文名	单位
孙国良	Sun Guoliang	新疆大学电气工程学院
伊力哈木.亚尔买买提	Yilihamu Yaermaimaiti	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
张宽	Zhang Kuan	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
吐松江.卡日	Tusongjiang.Kari	新疆大学电气工程学院
李振恩	Li Zhen-En	新疆大学电气工程学院
邸强	Di Qiang	新疆大学电气工程学院

摘要点击次数: 728

中文摘要:

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。

英文摘要:

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, 3σ criterion 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 direction 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.

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