基于小生境遗传算法与径向基代理模型的短期风电功率预测

刘沛汉; 尹翠; 贾娜; 樊小朝; 杨青斌

doi:10.19912/j.0254-0096.tynxb.2023-0615

PDF(1800 KB)

太阳能学报 ›› 2024, Vol. 45 ›› Issue (8) : 554-564. DOI: 10.19912/j.0254-0096.tynxb.2023-0615

基于小生境遗传算法与径向基代理模型的短期风电功率预测

刘沛汉¹, 尹翠¹, 贾娜², 樊小朝¹, 杨青斌³

作者信息 +

SHORT-TERM WIND POWER PRIDICTION BASED ON NICHE GENETIC ALGORITHM AND RADIAL BASIS SURROGATE MODEL

Liu Peihan¹, Yin Cui¹, Jia Na², Fan Xiaochao¹, Yang Qingbin³

Author information +

文章历史 +

摘要

为提高短期功率预测精度,以赋予风电被电网资产更大规模消纳的优势,建立一种基于主导特征影响因素和小生境遗传算法改进的径向基代理模型的滚动式短期（0~72 h）风电功率预测模型。首先,基于罚函数和排挤机制的小生境技术对传统基本遗传算法进行改进,以径向基代理模型（RBF）作为建模基础,利用改进后的遗传算法以反传误差极小为目标函数对RBF模型的连接权值进行优化,借助其寻优能力来获取最佳权值,以达成对RBF网络的改进和二次训练;然后,基于主导特征气象因素,结合改进的RBF模型最终建立N-SGA-RBF风电出力预测模型,对风电场连续3日0~72 h输出功率进行预测;最后,对N-SGA-RBF模型、RBF模型以及BP模型做预测结果趋势变化、各采样点绝对/相对误差分布、发电预测预报准确率和合格率的对比。以新疆东部某风电场实测数据进行算例验证分析,仿真结果表明,所建预测模型具有较高的精度。

Abstract

In order to improve the forecast accuracy of short-term power and promote more wind power being consumed by power grid,this paper establishes a rolling short-term （0-72 h） wind power prediction model based on dominant feature influencing factors and niche genetic algorithm improved neural network. Firstly, the niche technology based on penalty function and crowding mechanism is used to improve the traditional basic genetic algorithm. With the radial basis surrogate model （RBF） as the modeling basis, the improved genetic algorithm is used to optimize the connection weights of the RBF model with the minimum back-propagation error as the objective function. With its optimization ability, the optimal weights are obtained to achieve the improvement and secondary training of the RBF network; Then, based on dominant meteorological factors and an improved RBF model, an N-SGA-RBF wind power output prediction model is established to predict the output power of the wind farm for 0-72 hours for three consecutive days; Finally, the N-SGA-RBF model, RBF model, and BP model are compared for trend changes in prediction results, absolute and relative error distribution at each sampling point, accuracy and qualification rate of power generation prediction. An example verification analysis is conducted using measured data from a wind farm in eastern Xinjiang. The simulation results show that the established prediction model has high accuracy.

导出引用

刘沛汉, 尹翠, 贾娜, 樊小朝, 杨青斌. 基于小生境遗传算法与径向基代理模型的短期风电功率预测[J]. 太阳能学报. 2024, 45(8): 554-564 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0615

Liu Peihan, Yin Cui, Jia Na, Fan Xiaochao, Yang Qingbin. SHORT-TERM WIND POWER PRIDICTION BASED ON NICHE GENETIC ALGORITHM AND RADIAL BASIS SURROGATE MODEL[J]. Acta Energiae Solaris Sinica. 2024, 45(8): 554-564 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0615

中图分类号： TM614

参考文献

[1] 冉靖, 张智刚, 梁志峰, 等. 风电场风速和发电功率预测方法综述[J]. 数理统计与管理, 2020, 39(6): 1045-1059.
RAN J, ZHANG Z G, LIANG Z F, et al.Review of wind speed and wind power prediction methods[J]. Journal of applied statistics and management, 2020, 39(6): 1045-1059.
[2] 武煜昊, 王永生, 徐昊, 等. 风电输出功率预测技术研究综述[J]. 计算机科学与探索, 2022, 16(12): 2653-2677.
WU Y H, WANG Y S, XU H, et al.Survey of wind power output power forecasting technology[J]. Journal of frontiers of computer science and technology, 2022, 16(12): 2653-2677.
[3] 叶林, 路朋, 赵永宁, 等. 含风电电力系统有功功率模型预测控制方法综述[J]. 中国电机工程学报, 2021, 41(18): 6181-6198.
YE L, LU P, ZHAO Y N, et al.Review of model predictive control for power system with large-scale wind power grid-connected[J]. Proceedings of the CSEE, 2021, 41(18): 6181-6198.
[4] 苗长新, 李昊, 王霞, 等. 基于数据驱动和深度学习的超短期风电功率预测[J]. 电力系统自动化, 2021, 45(14): 22-29.
MIAO C X, LI H, WANG X, et al.Data-driven and deep-learning-based ultra-short-term wind power prediction[J]. Automation of electric power systems, 2021, 45(14): 22-29.
[5] 梁恩豪, 孙军伟, 王延峰. 基于自适应樽海鞘算法优化BP的风光互补并网发电功率预测[J]. 电力系统保护与控制, 2021, 49(24): 114-120.
LIANG E H, SUN J W, WANG Y F.Wind and solar complementary grid-connected power generation prediction based on BP optimized by a swarm intelligence algorithm[J]. Power system protection and control, 2021, 49(24): 114-120.
[6] 李福东, 曾旭华, 魏梅芳, 等. 基于聚类分析和混合自适应进化算法的短期风电功率预测[J]. 电力系统保护与控制, 2020, 48(22): 151-158.
LI F D, ZENG X H, WEI M F, et al.Short-term wind power forecasting based on cluster analysis and a hybrid evolutionary-adaptive methodology[J]. Power system protection and control, 2020, 48(22): 151-158.
[7] 王浩, 王艳, 纪志成. 基于SAIGM-KELM的短期风电功率预测[J]. 电力系统保护与控制, 2020, 48(18): 78-87.
WANG H, WANG Y, JI Z C.Short-term wind power forecasting based on SAIGM-KELM[J]. Power system protection and control, 2020, 48(18): 78-87.
[8] 田壁源, 刘琪, 张新燕, 等. 基于APSO-GSA和相关向量机的短期风电功率预测[J]. 电力系统保护与控制, 2020, 48(2): 107-114.
TIAN B Y, LIU Q, ZHANG X Y, et al.Short-term wind power prediction based on APSO-GSA and correlation vector machine[J]. Power system protection and control, 2020, 48(2): 107-114.
[9] 杨茂, 王达, 王小海, 等. 基于数据物理混合驱动的超短期风电功率预测模型[J/OL]. 高电压技术: 1-11[2023-07-10].
YANG M, WANG D, WAGN X H, et al.A ultra short term wind power prediction model based on data physics hybrid drive[J/OL]. High voltage engineering: 1-11[2023-07-10].
[10] 赵凌云, 刘友波, 沈晓东, 等. 基于CEEMDAN和改进时间卷积网络的短期风电功率预测模型[J]. 电力系统保护与控制, 2022, 50(1): 42-50.
ZHAO L Y, LIU Y B, SHEN X D, et al.Short-term wind power prediction model based on CEEMDAN and an improved time convolutional network[J]. Power system protection and control, 2022, 50(1): 42-50.
[11] 张越, 臧海祥, 程礼临, 等. 基于自适应时序表征和多级注意力的超短期风电功率预测[J]. 电力自动化设备, 2024, 44(2): 117-125.
ZHANG Y, ZANG H X, CHENG L L, et al.Ultra short term wind power prediction based on adaptive temporal representation and multi-level attention[J]. Electric power putomation equipment, 2024, 44(2): 117-125.
[12] 程江洲, 潘飞, 鲍刚. 基于RCC-GRU模型的超短期风电功率预测方法[J]. 计算机仿真, 2023, 40(2): 79-83.
CHENG J Z, PAN F, BAO G.Ultra-short-term wind power prediction method based on RCC-GRU model[J]. Computer simulation, 2023, 40(2): 79-83.
[13] LIU L, LIU J C, YE Y, et al.Ultra-short-term wind power forecasting based on deep Bayesian model with uncertainty[J]. Renewable energy, 2023, 205: 598-607.
[14] 崔杨, 王议坚, 黄彦浩, 等. 基于多元注意力框架与引导式监督学习的闭环风电功率超短期预测策略[J]. 中国电机工程学报, 2023, 43(4): 1334-1347.
CUI Y, WANG Y J, HUANG Y H, et al.Closed-loop wind power ultra-short-term forecasting strategy based on multi-attention framework and guided supervised learning[J]. Proceedings of the CSEE, 2023, 43(4): 1334-1347.
[15] 李大中, 李颖宇. 基于深度学习与误差修正的超短期风电功率预测[J]. 太阳能学报, 2021, 42(12): 200-205.
LI D Z, LI Y Y.Ultra-short term wind power prediction based on deep learning and error correction[J]. Acta energiae solaris sinica, 2021, 42(12): 200-205.
[16] 王晓东, 栗杉杉, 刘颖明, 等. 基于特征变权的超短期风电功率预测[J]. 太阳能学报, 2023, 44(2): 52-58.
WANG X D, LI S S, LIU Y M, et al.Ultra-short-term wind power prediction based on variable feature weight[J]. Acta energiae solaris sinica, 2023, 44(2): 52-58.
[17] 王愈轩, 梁沁雯, 章思远, 等. 基于LSTM-XGboost组合的超短期风电功率预测方法[J]. 科学技术与工程, 2022, 22(14): 5629-5635.
WANG Y X, LIANG Q W, ZHANG S Y, et al.An ultra-short-term wind power prediction method based on LSTM-XGboost combination[J]. Science technology and engineering, 2022, 22(14): 5629-5635.
[18] 王丽婕, 刘田梦, 王勃, 等. 基于奇异值分解与卡尔曼滤波修正多位置NWP的短期风电功率预测[J]. 太阳能学报, 2022, 43(12): 392-398.
WANG L J, LIU T M, WANG B, et al.Short-term wind power prediction based on SVD and Kalman filter correction of multi-position NWP[J]. Acta energiae solaris sinica, 2022, 43(12): 392-398.
[19] 杨茂, 孙志博, 苏欣. 基于混沌理论的风电功率超短期预测模式改进研究[J]. 中国电机工程学报, 2022, 42(22): 8117-8129.
YANG M, SUN Z B, SU X.Research on improvement of ultra-short-term wind power forecasting model based on chaos theory[J]. Proceedings of the CSEE, 2022, 42(22): 8117-8129.
[20] 杨茂, 彭天, 苏欣. 基于预测信息二维坐标动态划分的风电集群功率超短期预测[J]. 中国电机工程学报, 2022, 42(24): 8854-8864.
YANG M, PENG T, SU X.Ultra-short term wind power prediction based on two-dimensional coordinate dynamic division of prediction information[J]. Proceedings of the CSEE, 2022, 42(24): 8854-8864.
[21] 张淑清, 杨振宁, 姜安琦, 等. 基于EN-SKPCA降维和FPA优化LSTMNN的短期风电功率预测[J]. 太阳能学报, 2022, 43(6): 204-211.
ZHANG S Q, YANG Z N, JIANG A Q, et al.Short term wind power prediction based on EN-SKPCA dimensionality reduction and FPA optimizing LSTMNN[J]. Acta energiae solaris sinica, 2022, 43(6): 204-211.
[22] 师洪涛, 高天霁, 丁茂生, 等. 基于趋势聚类与决策树的风电功率多区间复合短期预测方法[J]. 太阳能学报, 2022, 43(4): 333-340.
SHI H T, GAO T J, DING M S, et al.Wind power multi-interval composite short-term prediction method based on trend clustering and decision tree[J]. Acta energiae solaris sinica, 2022, 43(4): 333-340.
[23] 王晓东, 鞠邦国, 刘颖明, 等. 基于QR-NFGLSTM与核密度估计的风电功率概率预测[J]. 太阳能学报, 2022, 43(2): 479-485.
WANG X D, JU B G, LIU Y M, et al.Probability prediction of wind power based on QR-NFGLSTM and kernel density estimation[J]. Acta energiae solaris sinica, 2022, 43(2): 479-485.
[24] 谢丽蓉, 王斌, 包洪印, 等. 基于EEMD-WOA-LSSVM的超短期风电功率预测[J]. 太阳能学报, 2021, 42(7): 290-296.
XIE L R, WANG B, BAO H Y, et al.Super-short-term wind power forecasting based on EEMD-WOA-LSSVM[J]. Acta energiae solaris sinica, 2021, 42(7): 290-296.
[25] 赵雅婷, 景超, 张兴忠. 基于多重注意力的双通道超短期风电功率预测[J]. 电网技术, 2023, 47(7): 2887-2897.
ZHAO Y T, JING C, ZHANG X Z.Dual-channel ultra-short-term wind power prediction based on multi-attention[J]. Power system technology, 2023, 47(7): 2887-2897.
[26] 李伟, 王冰, 曹智杰, 等. 基于混沌理论的鸡群改进算法及其在风电功率区间预测中的应用[J]. 太阳能学报, 2021, 42(7): 350-358.
LI W, WANG B, CAO Z J, et al.Application of ccso in wind power interval prediction[J]. Acta energiae solaris sinica, 2021, 42(7): 350-358.
[27] 王晓东, 苗宜之, 刘颖明, 等. 基于多分解策略和误差校正的超短期风电功率混合智能预测算法[J]. 太阳能学报, 2021, 42(6): 312-320.
WANG X D, MIAO Y Z, LIU Y M, et al.Hybrid intelligent prediction algorithm of ultra-short-term wind power based on multi-decomposition strategy and error correction[J]. Acta energiae solaris sinica, 2021, 42(6): 312-320.
[28] 宋峰, 王琳, 张小奇, 等. 含多主体微电网的循环神经网络预测及纳什博弈行为优化[J]. 电力系统及其自动化学报, 2023, 35(8): 136-142, 151.
SONG F, WANG L, ZHANG X Q, et al.RNN-LSTM prediction and Nash game behavior optimization for multi-agent microgrids[J]. Proceedings of the CSU-EPSA, 2023, 35(8): 136-142, 151.
[29] 张帅帅, 尹燕芳, 肖林京, 等. 基于门控循环神经网络的四足机器人静步态规划方法[J]. 吉林大学学报(工学版), 2023, 53(3): 902-912.
ZHANG S S, YIN Y F, XIAO L J, et al.Static gait planning method for quadruped robot based on gate recurrent neural network[J]. Journal of Jilin University(engineering and technology edition), 2023, 53(3): 902-912.