Wind Power Converter Fault Diagnosis Using Reduced Kernel PCA-Based BiLSTM

Khadija Attouri, Majdi Mansouri (), Mansour Hajji, Abdelmalek Kouadri, Kais Bouzrara and Hazem Nounou
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Khadija Attouri: Research Unit Advanced Materials and Nanotechnologies (UR16ES03), Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University, Kasserine 1200, Tunisia
Majdi Mansouri: Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha P.O. Box 23874, Qatar
Mansour Hajji: Research Unit Advanced Materials and Nanotechnologies (UR16ES03), Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University, Kasserine 1200, Tunisia
Abdelmalek Kouadri: Signals and Systems Laboratory, Institute of Electrical and Electronic Engineering, University M’Hamed Bougara of Boumerdes, Avevue of Independence, Boumerdes 35000, Algeria
Kais Bouzrara: Laboratory of Automatic Signal and Image Processing, National Engineering School of Monastir, Monastir 5035, Tunisia
Hazem Nounou: Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha P.O. Box 23874, Qatar

Sustainability, 2023, vol. 15, issue 4, 1-19

Abstract: In this paper, we present a novel and effective fault detection and diagnosis (FDD) method for a wind energy converter (WEC) system with a nominal power of 15 KW, which is designed to significantly reduce the complexity and computation time and possibly increase the accuracy of fault diagnosis. This strategy involves three significant steps: first, a size reduction procedure is applied to the training dataset, which uses hierarchical K-means clustering and Euclidean distance schemes; second, both significantly reduced training datasets are utilized by the KPCA technique to extract and select the most sensitive and significant features; and finally, in order to distinguish between the diverse WEC system operating modes, the selected features are used to train a bidirectional long-short-term memory classifier (BiLSTM). In this study, various fault scenarios (short-circuit (SC) faults and open-circuit (OC) faults) were injected, and each scenario comprised different cases (simple, multiple, and mixed faults) on different sides and locations (generator-side converter and grid-side converter) to ensure a comprehensive and global evaluation. The obtained results show that the proposed strategy for FDD via both applied dataset size reduction methods not only improves the accuracy but also provides an efficient reduction in computation time and storage space.

Keywords: wind energy converter (WEC) systems; fault detection and diagnosis (FDD); dataset reduction; kernel principal component analysis (KPCA); bidirectional long-short-term memory (BiLSTM) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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