Open AccessTrend Prediction of DC Measuring System Based on LSTM
Author(s) -
Ying Pei,
Lin Niu,
Haifeng Li,
Yajin Li,
Dong Yu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2083/3/032021
Subject(s) - computer science , long short term memory , series (stratigraphy) , state (computer science) , construct (python library) , term (time) , time series , artificial neural network , artificial intelligence , algorithm , machine learning , recurrent neural network , paleontology , physics , quantum mechanics , biology , programming language
The accuracy of DC measurement system directly affects the reliable operation of DC control and protection system. In order to improve the estimation and prediction of DC measurement system op-eration state, a trend prediction algorithm based on multi-dimensional analysis and long-term memory network is proposed. Based on the analysis of DC measurement principle, the DC measurement status is diagnosed and abnormal is identified by time series trend analysis and anomaly detection. The LSTM is used to construct a multi factor driving current prediction model, and the model is trained and an-alyzed based on the actual operation data. Compared with the traditional time series prediction model, the results indicate that the proposed method is more accurate, simple and effective, and can be applied to the prediction of driving current.