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AuthorYavuz, Levent
AuthorSoran, Ahmet
AuthorOnen, Ahmet
AuthorLi, Xiangjun
AuthorMuyeen S.M.
Available date2023-02-26T08:30:00Z
Publication Date2022
Publication NameCSEE Journal of Power and Energy Systems
ResourceScopus
URIhttp://dx.doi.org/10.17775/CSEEJPES.2020.03760
URIhttp://hdl.handle.net/10576/40400
AbstractThis paper proposes a new cost-efficient, adaptive, and self-healing algorithm in real time that detects faults in a short period with high accuracy, even in the situations when it is difficult to detect. Rather than using traditional machine learning (ML) algorithms or hybrid signal processing techniques, a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML algorithms. In the proposed method, the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization (PSO) weights. For this purpose, power system failures are simulated by using the PSCAD-Python co-simulation. One of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored information. Therefore, the proposed technique will be able to work on different systems, topologies, or data collections. The proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect. 2015 CSEE.
Languageen
PublisherChina Electric Power Research Institute
SubjectDecision tree (DT)
Ensemble machine learning algorithm
Fault detection
Islanding operation
K-Nearest Neighbor (kNN)
Linear discriminant analysis (LDA)
Logistic regression (LR)
Naive Bayes (NB)
Self-healing algorithm
TitleAdaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm
TypeArticle
Pagination1145-1156
Issue Number4
Volume Number8
dc.accessType Open Access


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