Open AccessResearch on power quality prediction of fluctuating load
Author(s) -
Xuri Sun,
Yanzhen Li,
Pengfei Shen
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/6/062013
Subject(s) - autoregressive integrated moving average , computer science , artificial neural network , predictive power , predictive modelling , quality (philosophy) , power quality , data mining , series (stratigraphy) , power (physics) , time series , power grid , machine learning , artificial intelligence , reliability engineering , engineering , paleontology , philosophy , physics , epistemology , quantum mechanics , biology
With the development of intelligent distribution network, power quality prediction is becoming more and more important. In this paper, the power quality prediction methods are studied and applied to a power grid. Firstly, on the basis of time series research, two mathematical models of ARIMA and BP neural network are derived and analyzed, and the corresponding power quality data prediction models are established. Then, the two models are applied to two datasets of different sizes to test and analyze their predictive effects. Based on the analysis and comparison of the prediction results, the improvement direction to further improve the prediction accuracy of the method is proposed.