The rapid growth of distributed photovoltaic (PV) has remarkable influence for the safe and economicoperation of power systems. In view of the wide geographical distribution and a large number of distributed PV power stations, the current situation is that it is difficult to access the current dispatch data network. According to the temporal and spatial characteristics of distributed PV, a graph convolution algorithm based on adaptive learning of adjacency matrix is proposed to estimate the real-time output of distributed PV in regional power grid. The actual case study shows that the adaptive graph convolution model gives different adjacency matrixes for different PV stations, which makes the corresponding output estimation algorithm have higher accuracy.
CHEN Liyue1 (陈利跃), HONG Daojian2 (洪道鉴), HE Xing3* (何星), LU Dongqi2 (卢东祁), ZHANG Qian2 (张乾), XIE Nina2 (谢妮娜), XU Yizhou2 (徐一洲), YING Huanghao2 (应煌浩)
. Distributed Photovoltaic Real-Time Output Estimation Based on
Graph Convolutional Networks[J]. Journal of Shanghai Jiaotong University(Science), 2024
, 29(2)
: 290
-296
.
DOI: 10.1007/s12204-022-2522-6
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