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Distributed Learning Algorithm for Distributed PV Large-Scale Access to Power Grid Based on Machine Learning

  • Zhen Lei
  • Yong-biao YangEmail author
  • Xiao-hui Xu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 301)

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.

Keywords

Operation efficiency Photovoltaic capacity Radial structure Power system 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.State Grid Jiangsu Electric Power CompanyNanjingChina
  2. 2.Southeast UniversityNanjingChina
  3. 3.China Electric Power Research InstituteNanjingChina

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