A new Fuzzy&Wavelet-based adaptive thresholding method for detecting PQDs in a hydrogen and solar-energy powered EV charging station. (28th February 2023)

• Record Type: Journal Article
• Authors: Bayrak, Gökay; Yılmaz, Alper; Çakmak, Recep
• Is Part Of: International journal of hydrogen energy. Volume 48:Number 18(2023)
• Page Start: 6855
• Page End: 6870
• Publication Date: 2023-02-28
• Subjects: Power quality -- Distributed generation -- Fuzzy logic decision making -- EV charge Stations -- Automated fault detection; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2022.08.067
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: This study presents a hybrid fuzzy decision-maker (FDM) and un-decimated wavelet transform (UWT)-based method for detecting power quality disturbances (PQDs) in a developed hydrogen and solar energy-powered electric vehicle (EV) charge station. The proposed adaptive FDM&UWT-based hybrid method eliminated the lack of performance of threshold-based signal analysis methods in noise-containing signals and it is implemented for a reliable PQD detection and integration in a developed microgrid. Also, the proposed method has eliminated the need for a processing-intensive filtering process to reduce noise from the signal. With this adaptive approach, detection errors in boundary conditions in threshold value methods are avoided and at the same time, cost and computational burden are minimized by using only the peak values in the detail coefficients of the voltage signal. The mean test accuracy is 96.13% for the FDM method using pyramidal UWT in noise-free conditions. Besides, the pyramidal UWT-FDM has a mean classification accuracy of 94.96% under 20–40 dB high-level noise conditions. The effectiveness of the UWT-FDM method is also tested using an experimental setup. The mean test accuracy for experimental data is 96.66%. Graphical abstract: Image 1 Highlights: An intelligent UWT&fuzzy-based PQD detection&classification method is proposed. A new adaptive thresholding approach using FDM is implemented for detecting PQDs. The method is performed in hydrogen and solar energy-powered EV charge station. The pyramidal-UWT technique is used for signal analysis. Classification accuracy is 94.96% under 20–40 dB high-level noise conditions.