Research Article
Distributed Learning Algorithm for Distributed PV Large-Scale Access to Power Grid Based on Machine Learning
@INPROCEEDINGS{10.1007/978-3-030-36402-1_47, author={Zhen Lei and Yong-biao Yang and Xiao-hui Xu}, title={Distributed Learning Algorithm for Distributed PV Large-Scale Access to Power Grid Based on Machine Learning}, proceedings={Advanced Hybrid Information Processing. Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21--22, 2019, Proceedings, Part I}, proceedings_a={ADHIP}, year={2019}, month={11}, keywords={Operation efficiency Photovoltaic capacity Radial structure Power system}, doi={10.1007/978-3-030-36402-1_47} }- Zhen Lei
Yong-biao Yang
Xiao-hui Xu
Year: 2019
Distributed Learning Algorithm for Distributed PV Large-Scale Access to Power Grid Based on Machine Learning
ADHIP
Springer
DOI: 10.1007/978-3-030-36402-1_47
Abstract
Due to the long prediction time and the large range of data filtering, the traditional algorithm has low system operation efficiency. For this reason, distributed learning based on machine learning is widely used to predict the power grid output. First, establish a grid output prediction model to limit the system’s line loss and transformer losses. Secondly, based on the distributed photovoltaic power generation output prediction model, the vector moment method and the information method are used to narrow the search space. Based on the data concentration and fitness function values, the calculation formula of voltage output prediction of distribution network nodes with distributed photovoltaics is derived to realize the power grid output prediction algorithm. Finally, it is proved by experiments that distributed PV large-scale access to power grid output prediction algorithm can effectively improve system operation efficiency.
