摘要
风电规模化并网技术的大力发展,进一步增大了对电力系统规划与运行的影响。现今,风电机组出力面临着波动的随机性以及不确定性的技术性问题,为了提高短期风电功率预测的精度,文中提出了一种结合基于群体适应度方差自适应变异的蝙蝠优化算法(AMBA)与BP神经网络算法,就短期风电功率进行精准预测。该模型根据群体适应度方差以及当前最优解的数值来定位当前最优个体的变异概率,并对全局最优个体进行t分布变异,对变异后的蝙蝠个体进行二次寻优。利用AMBA优化BP神经网络中包含的网络参数,进而提高了BP神经网络的预测精度。通过对实例进行分析,将AMBA-BP模型预测效果与其他模型预测结果相对比。结果表明,该模型能有效提高短期风电功率预测精度。
The influence degree of wind power large-scale grid-connected technology on planning and operation of power system is deepening day by day.Aiming at the randomness and uncertainty characteristics of the output fluctuation of wind turbines,in order to improve prediction accuracy of short-term wind power,a short-term wind power prediction method combined adaptive mutation bat optimization algorithm(AMBA)and BP neural network,which is based on the variation of population fitness variance is proposed in this paper.According to variance of population fitness and the size of the current optimal solution,the model determines the mutation probability of the current optimal individual and the t-distribution variation of the global optimal individual,and the secondary optimization of the mutated bat individuals.Network parameters of BP neural network are optimized by AMBA,and then,prediction accuracy of BP neural network is improved.By analyzing the example,prediction effect of AMBA-BP model is compared with other model prediction results.The results show that the model can effectively improve prediction accuracy of short-term wind power.
作者
徐鹏超
李琰
赵艳雷
Xu Pengchao;Li Yan;Zhao Yanlei(School of Electrical and Electronic Engineering,Shandong University of Technology,Zibo 255000,Shandong,China)
出处
《电测与仪表》
北大核心
2021年第4期121-127,共7页
Electrical Measurement & Instrumentation
基金
国家重点研发计划项目(2017YFB0902800)
国家电网公司科技项目(52094017003D)。
关键词
自适应变异
BP神经网络
蝙蝠算法
t分布变异
短期风电功率预测
adaptive mutation
BP neural network
bat algorithm
t-distribution variation
short-term wind power prediction
