Kernel‐PCA‐based single‐phase earth fault detection model using multilayer perceptron in deep learning

Autor:	Xueneng Su, Hua Zhang, Jian Zhang, Cheng Long, Xiaopeng Li, Yiwen Gao, Shilong Li
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	power system faults power systems fault features kernel principal component analysis multiple layer perceptron single‐phase earth fault Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841
Zdroj:	IET Generation, Transmission & Distribution, Vol 18, Iss 4, Pp 834-843 (2024)
Druh dokumentu:	article
ISSN:	1751-8695 1751-8687
DOI:	10.1049/gtd2.13117
Popis:	Abstract The rapid and accurate identification of single‐phase earth fault is of prime concern for many scholars from industry and academia, because its fault characteristics are extremely weak while the related potential hazard is extremely severe. In this context, this paper borrows from Deep Learning and in turn innovatively puts forward a multilayer perceptron (MLP)‐based single‐phase earth fault detection model augmented with kernel principal component analysis (KPCA). First, KPCA is applied to building fault feature extraction engineering via the transformation of fault features from low‐dimensional linear indivisible space to high‐dimensional one. Second, a MLP‐based model is used for basic model reference where many optimization strategies together with dropout technique are introduced and custom‐designed in tuning this model to achieve the best detection accuracy and convergence characteristic. Numerical studies demonstrate the superiority of the proposed KPCA‐MLP‐based single‐phase earth fault detection model.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/ed9f89d463e94d6eb3c31ce002b9884b Zobrazit plný text záznamu Plný text View record in DOAJ