典型文献
Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm
文献摘要：
This 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 Optimiza-tion(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.
文献关键词：
中图分类号：
作者姓名：
Levent Yavuz;Ahmet Soran;Ahmet ?nen;Xiangjun Li;S.M.Muyeen
作者机构：
Electrical and Computer Engineering Department in Abdullah Gül University,Kayseri 38080,Turkey;Department of Electrical and Computer Engineering,College of Engineering,Sultan Qaboos University,Al-Khoud,Muscat,123,Oman;State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems,China Electric Power Research Institute,Beijing 100192,China;Department of Electrical Engineering,Qatar University,Doha 2713,Qatar
文献出处：
引用格式：
[1]Levent Yavuz;Ahmet Soran;Ahmet ?nen;Xiangjun Li;S.M.Muyeen-.Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm)[J].中国电机工程学会电力与能源系统学报（英文版）,2022(04):1145-1156
A类：
B类：
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AB值：
0.626669
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