大型光伏电站发电功率预测算法设计

Design of Power Generation Prediction Algorithm for Large Scale Photovoltaic Power Station

近年来,由于我国电力系统的不断发展,光伏发电作为一种新型发电形式应用较为广泛。为满足各区域电网对大型光伏电站功率预测的准确性要求,对大型光伏电站发电功率预测算法进行设计。首先,通过建立LSTM的预测计算单元、创建FOA-Elman的发电功率预测算法模型、BP神经网络实现光伏电站发电功率的预测,完成电站发电功率预测算法设计;实验结果表明,该设计方法可以促使电站的功率具有更高的稳定性和安全性,同时加大电站对发电功率的控制。

In recent years,due to the continuous development of China’s power system,photovoltaic power generation has gradually become the main pillar of China’s power system.In order to meet the accuracy requirements of regional power grid for largescale photovoltaic power station power prediction,the generation power prediction algorithm of large-scale photovoltaic power station is designed.Firstly,by establishing the prediction calculation unit of LSTM,creating FOA Elman generation power prediction algorithm model and BP neural network to realize the generation power prediction of photovoltaic power station,the generation power prediction algorithm design of power station is completed;The experimental results show that the design method can make the power of the power station more stable and safe,and increase the control of power generation.