Open AccessPhotovoltaic Power System Fault Warning based on State Assessment
Author(s) -
Dong Wang,
Xiaorong Xie,
Zhixiong Na,
Wentao Shen,
Xuening Fan
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/632/3/032063
Subject(s) - photovoltaic system , reliability engineering , electric power system , fault (geology) , maximum power point tracking , computer science , grid connected photovoltaic power system , engineering , power (physics) , electrical engineering , physics , quantum mechanics , inverter , voltage , seismology , geology
The stability of photovoltaic power system is very important. A small fault in one component can cause a large damage on the whole power system. This paper aims to propose a method to evaluate the stability of photovoltaic power system so that we could take measures in advance to avoid greater losses. This paper studies how we assess the status of a power grid. To assess the status of power grid, we select some fault which is often occur in photovoltaic power system. Then we build a fault tree based on the photovoltaic power plant network. Finally, we substitute the probability of various faults into the fault tree for analysis and prediction. According to probability of failure at this moment and coefficient, we can calculate how the system running at this moment. In this paper, the early fault warning of photovoltaic power system: firstly use machine learning methods to pre-process the original data, eliminate redundancy and noise, then run regression analysis on pre-processed data and get the failure rate of a certain time. Take the example of a photovoltaic power system in China, through learning and verification of fault data, our method can effectively realize the early fault warning of photovoltaic power system.