首页|基于LSSVM改进AVOA的风电功率预测应用

基于LSSVM改进AVOA的风电功率预测应用

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  • 国家科技期刊平台
  • NETL
  • NSTL
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提高风电功率预测精度是减小风电对电网影响的有效手段。为此，提出了一种改进非洲秃鹫优化算法(African vulture optimization algorithm，AVOA)构建多目标优化最小二乘支持向量机(Least squares support vector machine，LSSVM)预测模型。首先，利用变分模态分解(Variational modal decomposition，VMD)将原始风功率时间序列有效分解为一定数量的固有模态分量(Intrinsic modal components，IMFs)。其次，以Tent混沌映射代替种群初始化中的随机数并引入带精英策略的非支配排序及拥挤度算子改进的AVOA算法多目标优化LSSVM，对每个分量进行预测。最后，利用TMOALSSVM方法分别对各分量集成加和得到最终的预测结果。仿真结果表明，基于Tent混沌映射改进的非洲秃鹫优化算法与加入带精英策略的非支配排序、拥挤度算子改进的优化算法分别为单目标和多目标预测的最优模型。其中，本研究所构建的模型在四个季节中风功率值三个平均误差值均为最小，RMSE、MAE以及MAPE值分别为0。0694、0。0545与0。0211;四个季节中的Ds统计量平均值为0。9902，统计值最大。该模型将方向预测精度作为多目标优化函数，在横向精度和纵向精度上均对四个季节的风功率值进行了有效预测，更快更准确地找到了当前约束条件下的最优pareto解集，由此证明了该方法在风电功率预测技术发展领域具有一定科学意义。
Improved AVOA based on LSSVM for wind power prediction
Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs) using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM) method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.

African vulture optimization algorithm(AVOA)least squares support vector machine (LSSVM)variational mode decomposition (VMD)multi-objective predictionwind power

张忠林、魏凡、闫光辉、马海云

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兰州交通大学电子与信息工程学院,甘肃兰州 730070

天水师范学院物理与信息科学学院,甘肃天水 741001

非洲秃鹫优化算法 最小二乘支持向量机 变模态分解 多目标预测 风电功率

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaScience and Technology Plan of Gansu ProvinceScience and Technology Plan of Gansu Province

616620426206204921JR7RA28821JR7RE174

2024

10.62756/jmsi.1674-8042.2024036

测试科学与仪器
中北大学

测试科学与仪器

影响因子：0.111
ISSN：1674-8042
年,卷(期)：2024.15(3)