Open AccessResearch on health assessment of electric power information system based on deep belief networks and cluster analysis
Author(s) -
Haiqing Xu,
Hongfa Li,
Shitong Chen,
Xiaohua Wu
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 - 10.1088/1755-1315/675/1/012123
Subject(s) - computer science , cluster analysis , deep belief network , entropy (arrow of time) , data mining , artificial intelligence , service (business) , electric power system , cluster (spacecraft) , support vector machine , service system , machine learning , power (physics) , deep learning , physics , programming language , economy , quantum mechanics , economics
It has been a hot issue for a long time to effectively ensure the safe and stable operation of the power service information system and make a reasonable evaluation of the healthy operation of the power service information system. Without relying too much on the subjective experience and scoring of experts, this paper introduces a health assessment method based on deep belief networks and cluster analysis for the power service information system. Firstly, cluster analysis is used to obtain corresponding cluster centers and evaluation index sets based on the selected evaluation indexes of the information system. Secondly, the entropy weight method is adopted to score different clustering categories and obtain the corresponding health evaluation categories. Finally, based on the score of the healthy operation, the samples are supervised and trained by the deep belief network model, and then the objective comprehensive evaluation of the system is completed. Experiment results show that this model is more accurate than traditional SVM, KNN, random forest and LSTM. The proposed model has theoretical significance and practical value for the healthy operation evaluation of the power service information system.