Bayrak, GokayYilmaz, AlperCakmak, Recep2026-02-122026-02-1220230360-31991879-3487https://doi.org/10.1016/j.ijhydene.2022.08.067https://hdl.handle.net/20.500.12885/6665This 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 condi-tions in threshold value methods are avoided and at the same time, cost and computa-tional 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%.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.eninfo:eu-repo/semantics/closedAccessPower qualityDistributed generationFuzzy logic decision makingAutomated fault detectionEV charge StationsArticle10.1016/j.ijhydene.2022.08.067481868556870WOS:0009737913000012-s2.0-85137729009Q1Q1