摘要
由于当前方法对发电数据进行采集时,没有对发电数据的缺失值进行修复,存在缺失数据修复前数据采集精度差、与实际采集数据相差多的问题。该文提出一种基于时间序列的分布式光伏电站发电数据采集方法。根据光伏电站受到的不同影响,构建了负荷异常值类型的时间序列模型,利用该模型对异常数据负荷点进行剔除,由于剔除后的数据存在缺失值,因此对其进行修复;根据修复结果,采用BP神经网络对发电数据进行采集。实验结果表明,通过对该方法进行缺失数据修复前后对比测试、不同方法与实际指标数据采集测试,验证了该方法的有效性强、实用性高。
Because the current method does not repair the missing value of power generation data when collecting power generation data,there are some problems,such as poor data acquisition accuracy before missing data repair and much difference from the actual collected data.Therefore,this paper proposes a distributed photovoltaic power station generation data acquisition method based on time series.According to the different effects on the photovoltaic power station,this method constructs a time series model of load abnormal value type,and uses this model to eliminate the abnormal data load points.Because the deleted data has missing values,it is repaired.According to the repair results,the BP neural network is used to collect the power generation data,so as to complete the collection of power generation data.The experimental results show that the method is effective and practical through the comparative test before and after missing data repair,and the data collection test of different methods and actual indicators.
作者
王婧骅
张娟
赵婉茹
陆萍
张云飞
WANG Jinghua;ZHANG Juan;ZHAO Wanru;LU Ping;ZHANG Yunfei(State Grid Shanghai Electric Power Company,Shanghai 200030,China)
出处
《电网与清洁能源》
北大核心
2022年第6期137-142,共6页
Power System and Clean Energy
基金
上海市重点实验室专项基金项目(05DZ33205)。
关键词
时间序列
分布式光伏电站
发电数据采集
BP神经网络
time series
distributed photovoltaic power station
power generation data collection
BP neural network
