摘要
随着风力发电和光伏发电系统的大规模应用,风光功率的不确定性给电力系统的稳定运行造成了严重的影响,而风光功率的准确预测可有效降低其带来的不良影响。在对风力发电和光伏发电功率特性分析的基础上,文中将小波神经网络应用于风光功率的预测,为进一步提高小波神经网络预测的准确度,对其训练更新算法及初值设置方法进行了相应的改进,通过风光功率的预测实例的对比分析验证了文中方法的有效性和优越性。该研究成果可为风力发电和光伏发电的预测提供有效的技术参考和理论指导。
With the large-scale application of wind power generation and photovoltaic power generation system,the uncertainty of wind-wind power has a serious impact on the stable operation of power system,and the accurate prediction of wind-solar power can effectively reduce its adverse effects.Based on the analysis of the power characteristics of wind power generation and photovoltaic power generation,wavelet neural network is applied to the prediction of wind-solar power in this paper.To further improve the accuracy of wavelet neural network prediction,the training update algorithm and the initial value setting method are improved.The effectiveness and superiority of this method are verified by the comparison and analysis of the forecast examples of wind-solar power.It can provide effective technical reference and theoretical guidance for the prediction of wind power generation and photovoltaic power generation.
作者
杨银国
李博
谭嫣
朱誉
熊欢
YANG Yin-guo;LI Bo;TAN Yan;ZHU Yu;Xiong Huan(Electricity Control Center of Guangdong Power Grid Co.,Ltd.,Guangzhou 510600,China;NARI Group Corporation(State Grid Electric Power Research Institute),Nanjing 211000,China)
出处
《信息技术》
2020年第2期98-102,共5页
Information Technology
关键词
风力发电
光伏发电
小波神经网络
功率预测
wind power generation
photovoltaic power generation
wavelet neural network
power prediction
