An accurate multiple cognitive classifier system for incipient short-circuit fault detection in induction generators

Karatzinis, Georgios; Boutalis, Yiannis S.; Karnavas, Yannis L.

doi:10.1007/s00202-021-01445-9

Cited by 2 publications

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Employing classical machine learning algorithms, such as Support Vector Machine (SVM), shallow neural networks, genetic algorithm, fuzzy logic, and decision trees for ITF detection in induction machines has a long history [18][19][20][21][22][23][24][25]. However, over the last 2 decades, the new concept of Deep Learning (DL) brought about an evolution in the field of machine learning [26].…”

Section: Introductionmentioning

confidence: 99%

Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed Induction Generator using deep learning

Alipoor

Mirbagheri

Moosavi

et al. 2022

IET Electric Power Appl

View full text Add to dashboard Cite

Wind turbines are increasingly expanding worldwide and Doubly‐Fed Induction Generator (DFIG) is a key component of most of them. Stator winding fault is a major fault in this equipment and its incipient detection is of vital importance. However, there is a paucity of research in this field. In this study, a novel machine learning‐based method is proposed for incipient detection of inter‐turn short‐circuit fault (ITF) in the DFIG stator based on the current signals of the stator. The proposed method makes use of state‐of‐the‐art deep learning methods along with conventional signal processing tools and general machine learning techniques. More specifically, the incipient fault detection problem is regarded as a multi‐class classification problem and a Long Short‐Term Memory network, which is more appropriate for time‐series data is utilised for inference. Furthermore, a variant of the celebrated Empirical mode Decomposition analysis tool is used to extract some well‐known statistical features among which the most informative ones are selected using a new feature selection method. Our tests using experimental data in steady‐state conditions show that the proposed method can accurately detect ITF fault at its initial stage when only one turn is shorted. Moreover, its performance is considerably higher than that of a variety of machine learning‐based methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed Induction Generator using deep learning

Alipoor

Mirbagheri

Moosavi

et al. 2022

IET Electric Power Appl

View full text Add to dashboard Cite

show abstract

Fuzzy Cognitive Networks in Diverse Applications Using Hybrid Representative Structures

Karatzinis,

Apostolikas,

Boutalis

et al. 2023

Int. J. Fuzzy Syst.

View full text Add to dashboard Cite

Fuzzy cognitive networks (FCNs) arose from traditional fuzzy cognitive maps (FCMs) to have the advantage of guaranteed convergence to equilibrium points, thus being more suitable than conventional FCMs for a variety of pattern recognition and system identification tasks. Moreover, recent developments led to FCNs with functional weights (FCNs-FW), as a significant FCNs enhancement in terms of storage requirements, efficiency and less human intervention requirements. In this paper we proceed further by introducing hybrid deep learning structures, interweaving FCNs-FW with well established deep neural network (DNN) representative structures and apply the new schemes on a variety of pattern recognition and time series prediction tasks. More specifically, after discussing general issues related to the construction of deep learning structures using FCNs-FW we present three hybrid models, which combine the FCN-FW with convolutional neural networks (CNNs), echo state networks (ESNs) and AutoEncoder (AE) schemes, respectively. The hybrid schemes are tested on diverse benchmark data sets and prove that FCN-FW based hybrid schemes perform equally well or better than state-of-the-art DNN-based schemes, paving thus the way for using cognitive networks to deep learning representative structures.

show abstract

An accurate multiple cognitive classifier system for incipient short-circuit fault detection in induction generators

Cited by 2 publications

References 44 publications

Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed Induction Generator using deep learning

Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed Induction Generator using deep learning

Fuzzy Cognitive Networks in Diverse Applications Using Hybrid Representative Structures

Contact Info

Product

Resources

About