基于改进深度学习的光伏电站发电量精准估计
Accurate Estimation of Photovoltaic Power Generation Based on Modified Deep Learning
幸荣霞1
作者信息
- 1. 国网浙江省电力有限公司杭州市临安区供电公司,浙江 杭州 311300
- 折叠
摘要
为实现对电站发电量的精准估计与测算,引入改进深度学习算法,以某光伏发电站为例,进行发电量精准估计方法的设计研究.计算光伏电站太阳辐照量,收集光伏电站功率、电流、电压等发电数据以及太阳辐射强度、温度、湿度等相关气象数据,并对收集到的光伏电站发电信息进行预处理;为排除光伏组件损失对发电量估算的影响,先进行光伏组件损失分析,再以此为依据对光伏电站发电量进行估计.实例应用结果表明该设计方法在应用中可以有效控制光伏电站发电量估计误差,提高发电量估计值的准确性与可靠性.
Abstract
In order to achieve accurate estimation and calculation of power generation in power plants,a modified deep learning algorithm is introduced.Taking a photovoltaic power plant as an example,a design and research on accurate esti-mation methods of power generation is carried out.The solar radiation amount of photovoltaic power plants is calculated.Power generation data of photovoltaic power plants,including power,current,voltage,and related meteorological data such as solar radiation intensity,temperature,humidity,etc.,are collected and subsequently preprocessed.To eliminate the impact of photovoltaic module loss on power generation estimation,a photovoltaic module loss analysis is conducted first,based on which accurate estimation of the power generation of photovoltaic power plants can be achieved.The exper-imental results of practical applications show that the designed method can effectively control the estimation error of pho-tovoltaic power generation,and improve the accuracy and reliability of power generation estimation values.
关键词
改进深度学习/辐照量/预处理/精准估计/发电量/光伏电站Key words
modified deep learning/irradiation amount/pre-processing/accurate estimation/power generation/photo-voltaic power plants引用本文复制引用
出版年
2024
NETL
NSTL
万方数据