基于最优深度信念网络的高比例分布式光伏发电数据虚拟采集方法研究
Research on Virtual Collection Method for High Proportion of Distributed Photovoltaic Power Generation Data Based on Optimal Deep Belief Network
张华 1李世龙 1龙呈 1高艺文 1苏学能 1李明俊1
作者信息
- 1. 国网四川省电力公司电力科学研究院,四川 成都 610041
- 折叠
摘要
为了解决配电站的高比例分布式光伏数据难以采集问题,提出了一种基于最优深度信念网络的分布式光伏数据虚拟采集方法.最优深度信念网络包括两部分,分别为基本深度信念网络与自适应萤火虫算法.其中,自适应萤火虫算法被用于估计深度信念网络的输出权重矩阵.首先,所提出的分布式光伏数据虚拟采集方法,可以实现同一光伏电站在仅 1 座分布式光伏设备具有完备数据采集装置情形下,完成区域范围内所有分布式光伏设备数据的虚拟采集;然后,以区域范围内 100 座分布式光伏设备对所提出的分布式光伏数据虚拟采集方法进行了验证.
Abstract
In order to solve the difficulty of collecting high proportion of distributed photovoltaic(PV)data in distribution station,a virtual collection method for distributed PV data based on optimal deep belief network is proposed.The optimal deep belief network consists of two parts,namely basic deep belief network and adaptive firefly algorith,in which the adaptive firefly algorithm is used to estimate the output weight matrix of deep belief networks.Firstly,the proposed virtual collection method for distributed PV data can realize the virtual data acquisition of all distributed PV equipment in the same PV power station under the condition that only one distributed PV equipment has a complete data acquisition device.And then,the proposed virtual collection method for distributed PV data is verified with 100 distributed PV devices in a region.
关键词
分布式光伏/深度信念网络/萤火虫算法/虚拟采集Key words
distributed photovoltaic/deep belief network/firefly algorithm/virtual collection引用本文复制引用
出版年
2024
NETL
NSTL
万方数据