Open AccessClassification and identification of electric shock current for safety operation in power distribution network
Author(s) -
Liu Yongmei,
Du Songhuai,
Sheng Wanxing
Publication year - 2020
Publication title -
iet cyber‐physical systems: theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 7
ISSN - 2398-3396
DOI - 10.1049/iet-cps.2019.0072
Subject(s) - support vector machine , adaboost , pattern recognition (psychology) , artificial intelligence , identification (biology) , computer science , artificial neural network , machine learning , botany , biology
Electric shock current identification is essential for the safety in power distribution network. Moreover, as different categories of object have different electric shock current characteristic, a classification model for shock current is essential to be proposed before identification. Therefore, the authors proposed a two‐stage framework, including the AdaBoost for the classification and an improved support vector machine (SVM) method for the identification. In the classification stage, the AdaBoost learns the hidden pattern of different electric shock current and generates a predictive model for current classification. Based on the classification results, a fusion method called SVM–NN is proposed in the identification stage, which is based on SVM and neural network (NN) to make fusion determination. The SVM–NN takes advantages of SVM and NN for integration analysis. Based on real data, these classification and identification methods are evaluated. Results show that the proposed method can significantly improve the identification accuracy of electric shock current signal comparing to traditional methods.