基于IAFSA-BPNN的短期风电功率预测  

IAFSA-BPNN for wind power probabilistic forecasting

作　　者：张颖超[1,2] 王雅晨[1] 邓华[1,2] 熊雄[2] 陈浩[1] 

机构地区：[1]南京信息工程大学信息与控制学院,江苏南京210044 [2]南京信息工程大学气象灾害预报预警与评估协同创新中心,江苏南京210044

出　　处：《电力系统保护与控制》2017年第7期58-63,共6页Power System Protection and Control

基　　金：国家自然科学基金项目(41675156);江苏省高校优势学科建设工程资助项目(PAPD);江苏省六大人才高峰项(WLW-021)共同资助~~

摘　　要：为提高短期风电功率预测精度,提出一种基于IAFSA-BPNN的短期风电功率预测方法。该方法通过改进的人工鱼群算法来优化BP神经网络的权值和阈值,从而提高BP神经网络的收敛速度和泛化能力。利用2014年上海某风场实测数据对新算法进行检验。试验结果表明,改进的人工鱼群算法一定程度上克服了原算法后期搜索的盲目性较大,收敛速度减慢,搜索精度变低的缺陷。IAFSA-BPNN混合算法在预测的稳定性和精度、收敛速度等方面优于BPNN、AFSA-BPNN算法。IAFSA-BPNN算法不仅能提高短期风电功率预测的精度,而且改善了预测结果稳定性。A method based on Improvement Artificial Fish Swarm Algorithm （1AFSA）-BP neural network algorithm is presented for improving the accuracy of short-term wind power forecasting. It optimizes the weights and thresholds of BPNN and improves the BPNN generalization capacity and the rate of convergence. By using the historical data of a wind farm of Shanghai in 2014, IAFSA is proposed to overcome the defects of traditional Artificial Fish Swarm Algorithm such as the blindness of searching, slow convergence speed and low searching precision at the later stage. The simulation result compared with BP neural network and AFSA-BPNN algorithm shows that the IAFSA-BPNN algorithm can not only improve the prediction accuracy and stability, but also shorten the model＇s rate of convergence, and improve the precision and stability in short-term wind power forecasting.

关 键 词：短期风电功率预测 人工鱼群算法 BP神经网络 IAFSA-BPNN 

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]