Multiple power quality events recognition and classification based on adaptive Eigen-based type-2 fuzzy logic algorithm

This work proposes an Eigen-based type-2 fuzzy algorithm for analysis of power signal disturbances. The power quality disturbances are random and non-linear by nature so the role of a particle filter is to estimate various parameters such as amplitude and phase. Particle filtering is now the most po...

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Vydané v:Iran Journal of Computer Science (Online) Ročník 5; číslo 1; s. 55 - 68
Hlavný autor: Rahul
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 01.03.2022
Predmet:
Artificial Intelligence
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Mathematics of Computing
Original Article
Software Engineering/Programming and Operating Systems
Theory of Computation
Particle filtering
Estimation of parameters
Power quality
Type-2 fuzzy
Eigen values
ISSN:2520-8438, 2520-8446
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Popis
Shrnutí:This work proposes an Eigen-based type-2 fuzzy algorithm for analysis of power signal disturbances. The power quality disturbances are random and non-linear by nature so the role of a particle filter is to estimate various parameters such as amplitude and phase. Particle filtering is now the most popular for tracking and signal processing. It has gained popularity in the signal processing by virtue of its ability to deal with non-linear and non-Gaussian systems.Our approach is to define an orthogonal matrix, for multiple power quality events then to evaluate Eigen values of estimated parameters. These Eigen values play an important role in detection of power quality events the estimated parameters and Eigen values applied as input to type-2 fuzzy for classification of power quality events. Finally robustness of algorithm in terms of improved accuracy and correctness with actual industrial data set of power quality events is compared under three different SNR conditions of 30, 20 and 10 dB with support vector machine, neural network based techniques and type-1 fuzzy logic based classification methods. High accuracy and less computation time make this algorithm robust and a suitable choice for non-linear and non-stationary power quality events analysis.
ISSN:2520-8438
2520-8446
DOI:10.1007/s42044-021-00088-0