Open AccessEvaluation of time series methods for distribution system state forecasting.
Author(s) -
C. M. Thasnimol,
R. Rajathy
Publication year - 2021
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/1070/1/012051
Subject(s) - computer science , estimator , state (computer science) , series (stratigraphy) , long short term memory , electric power system , time series , estimation , state vector , random forest , automation , artificial intelligence , machine learning , data mining , power (physics) , artificial neural network , algorithm , recurrent neural network , statistics , mathematics , engineering , mechanical engineering , paleontology , physics , systems engineering , quantum mechanics , classical mechanics , biology
High proliferation of DERs in the distribution system necessitates forecasting ability for state estimator. A dynamic state estimation is a fundamental tool for realizing distribution system automation. Selection of appropriate methodological framework is an essential step before implementing dynamic state estimation. We applied Vector Error Correction Model (VECM), Random Forest and Long Short Term Memory Network (LSTM) time series models to dynamic power system state estimation problem and found out that the LSTM model outperforms the other two in prediction accuracy.