A PV generation data reconstruction method based on improved super-resolution generative adversarial network
A PV generation data reconstruction method based on improved super-resolution generative adversarial network
复制标题
一种基于改进超分辨率生成对抗网络的光伏发电数据重构方法
DOI：
10.1016/j.ijepes.2021.107129
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发表时间：
2021
影响因子：
5.2
通讯作者：
Chen Feixiong
中科院分区：
文献类型：
--
作者：
Zhang Chengsheng;Shao Zhenguo;Jiang Changxu;Chen Feixiong
With the growing penetration of solar photovoltaic (PV) generation, advanced data analysis methods have been applied to the smart grid operation. However, the low-temporal-resolution PV generation data limits the utilization of the data analysis methods, because the low-temporal-resolution PV generation data contains too little information. On the other hand, the existing data reconstruction methods are less than satisfactory in reconstructing high-temporal-resolution PV generation data from low-temporal-resolution data, since most of them cannot fully capture the characteristics of PV generation data. To address this issue, a PV generation data reconstruction method based on an improved super-resolution generative adversarial network is proposed in this paper. First, a data-image construction method is proposed to encode the PV generation data into the so-called data-images. Furthermore, we develop a data-image super-resolution generative adversarial network (DISRGAN) model, and the data-images are used to train the DISRGAN model. Finally, based on the trained DISRGAN model, a general framework is developed to reconstruct high-temporal-resolution PV generation data from low-temporal-resolution data. Numerical experiments have been carried out based on PV generation data from the State Grid Corporation of China, to reconstruct the high-temporal-resolution data of irradiance and PV power from low-temporal-resolution data, respectively. The results demonstrate the superior performance of the proposed framework compared with a series of state-of-the-art methods.
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影响因子：
9.6
作者：
S. M. Sha;ul Alam;B. Natarajan;A. Pahwa
通讯作者：
S. M. Sha;ul Alam;B. Natarajan;A. Pahwa
电网中储能系统的最佳规模和布局：最先进的一站式手册
影响因子：
9.4
作者：
Yang Bo;Wang Junting;Chen Yixuan;Li Danyang;Zeng Chunyuan;Chen Yijun;Guo Zhengxun;Shu Hongchun;Zhang Xiaoshun;Yu Tao;Sun Liming
通讯作者：
Sun Liming
DOI：
10.1109/pesgm.2015.7286366
发表时间：
2015-07
期刊：
2015 IEEE Power & Energy Society General Meeting
影响因子：
--
作者：
Fang Zhang;Lin Cheng;Xiong Li;Yuanzhan Sun;Wenzhong Gao;Weixing Zhao
通讯作者：
Fang Zhang;Lin Cheng;Xiong Li;Yuanzhan Sun;Wenzhong Gao;Weixing Zhao
DOI：
10.1109/icccnt56998.2023.10306417
发表时间：
2022-02
期刊：
2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)
影响因子：
--
作者：
Gilad Cohen;Raja Giryes
通讯作者：
Gilad Cohen;Raja Giryes
DOI：
10.1109/cisp-bmei.2017.8301942
发表时间：
2017-10
期刊：
2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
影响因子：
--
作者：
Lejun Yu;Siming Cao;Jun He;Bo Sun;Feng Dai
通讯作者：
Lejun Yu;Siming Cao;Jun He;Bo Sun;Feng Dai