| Issue |
E3S Web Conf.
Volume 182, 2020
2020 10th International Conference on Power, Energy and Electrical Engineering (CPEEE 2020)
|
|
|---|---|---|
| Article Number | 03002 | |
| Number of page(s) | 6 | |
| Section | Energy and Energy Storage Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202018203002 | |
| Published online | 31 July 2020 | |
Power Reconstructing Method of Distributed Photovoltaic Based on the Temporal and Spatial Correlation
1 State Key Lab of Control and Simulation of Power Systems and Generation Equipment, Department of Electrical Engineering, Tsinghua University, 100084 Haidian District in Beijing, China
2 State Grid Jibei Electric Power Company, 100031 Xicheng District in Beijing, China
* Corresponding author: 363524255@qq.com
55GW distributed photovoltaic have been installed in China, but nearly half are connected to the low voltage level of 380V, without real-time power data acquisition. The sequential power data is needed to be reconstructed based on some related monitoring data. Current researches focus on outliers recovery, but not reconstruction from none. This paper explores the temporal and spatial correlation of power between adjacent centralized photovoltaic stations and proposes a large-scale missing power data reconstructing method based on the time-delay power correlation, the spatial geometric characteristics of stations and the thought of ensemble learning. Finally, we verify the effectiveness of the proposed method by simulation based on the real photovoltaic power data. The proposed method can get the better effect of data reconstructing compared with the traditional method, which only use the power curves of the nearest CP station to reconstruct the power curves of the DP station according the capacity conversion.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.