An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization

Majdi Mansouri (), Khaled Dhibi, Hazem Nounou and Mohamed Nounou
Additional contact information
Majdi Mansouri: Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha P.O. Box 23874, Qatar
Khaled Dhibi: 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
Mohamed Nounou: Chemical Engineering Program, Texas A&M University at Qatar, Doha P.O. Box 23874, Qatar

Sustainability, 2022, vol. 14, issue 18, 1-11

Abstract: The current paper proposes intelligent Fault Detection and Diagnosis (FDD) approaches, aimed to ensure the high-performance operation of Wind energy conversion (WEC) systems. First, an efficient feature selection algorithm based on particle swarm optimization (PSO) is proposed. The main idea behind the use of the PSO algorithm is to remove irrelevant features and extract only the most significant ones from raw data in order to improve the classification task using a neural networks classifier. Then, to overcome the problem of premature convergence and local sub-optimal areas when using the classical PSO optimization algorithm, an improved extension of the PSO algorithm is proposed. The basic idea behind this proposal is to use the Euclidean distance as a dissimilarity metric between observations in which a single observation is kept in case of redundancies. In addition, the proposed reduced PSO-NN (RPSO-NN) technique not only enhances the results in terms of accuracy but also provides a significant reduction in computation time and storage cost by reducing the size of the training dataset and removing irrelevant and redundant samples. The experimental results showed the robustness and high performance of the proposed diagnosis paradigms.

Keywords: wind energy conversion (WEC); fault diagnosis; particle swarm optimization (PSO); neural network (NN); euclidean distance (ED) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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