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- W4308596985 abstract "AbstractIslanding detection has become an important issue for interconnected power distribution systems. The main objective of the paper is to develop a new error correcting method which is reliant on the results of the pre-established classifiers. The measurements from different locations of the laboratory-based distribution system are collected, and the dimensionality is reduced using any one of the dimension reduction algorithms, i.e. principal component analysis, probabilistic principal component analysis, kernel principal component analysis and Andrews method. After dimension reduction, comparative analysis of classifiers for islanding detection in a laboratory-based microgrid has been established. The algorithms show unique characteristics when tested on the basis of decision surface, probability of classification and confusion matrix. The dimension reduction times and the classification training times are compared. Eventually, a novel error correcting output code has been implemented. The dimension reduction process used in this error correcting codes is a combination of kernel principal component and Andrews method. This error correcting method has been proved to be much beneficial for improving the accuracy of any type of classifiers for islanding detection.KeywordsClassificationDimension reductionError correcting output codePower system analysisMicrogridIslanding detection" @default.
- W4308596985 created "2022-11-12" @default.
- W4308596985 creator A5083072010 @default.
- W4308596985 date "2022-11-09" @default.
- W4308596985 modified "2023-10-14" @default.
- W4308596985 title "Performance Assessment and Improvement of Classifiers Using Error Correcting Output Code for Islanding Detection in Microgrid" @default.
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- W4308596985 doi "https://doi.org/10.1007/978-981-19-4971-5_45" @default.
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