Yilmaz, AlperAtesci, TolgaMeral, HasanBayrak, Gokay2026-02-082026-02-0820250278-00461557-9948https://doi.org/10.1109/TIE.2024.3436549https://hdl.handle.net/20.500.12885/5912The installation of electric vehicle charging stations (EVCSs) that are powered by renewable energy sources has been growing rapidly. However, this has raised a crucial issue regarding the quality of power supplied to these stations. Due to the intermittent nature of renewable energy sources and the high-power requirements of EV charging, power quality disturbances (PQDs) occur more. This study proposes a new intelligent PQD classification method that considers feature extraction/selection based on pyramidal undecimated wavelet transform (p-UWT) and minimum redundancy maximum relevance (mRMR). The feature vector, derived through the application of mRMR, comprises a mere ten elements. The p-UWT-mRMR combination overcomes the problem of noise sensitivity inWTs. In addition, Bayesian optimization and UWT-mRMR have addressed hyperparameter selection difficulties and overfitting in support vector machine models. The proposed method demonstrated an impressive classification accuracy of 99.55% when faced with 30-dB noise. A prototype test platform is developed with EVCS-integrated PV systems in the laboratory to verify the performance of the proposed method in real-time cases. Dynamic analysis revealed that all PQDs have runtimes ranging from 5 to 10ms in experiments. The proposed method has been validated on a dataset of over 20 000 real-world signals with a test accuracy of 99.11%.eninfo:eu-repo/semantics/closedAccessFeature extractionVectorsTransformsReal-time systemsSupport vector machinesNoiseDiscrete wavelet transformsElectric vehicle charging station (EVCS)machine learningminimum redundancy maximum relevance (mRMR)power quality (PQ)undecimated wavelet transform (UWT)Article10.1109/TIE.2024.343654972326222632WOS:0012927698000012-s2.0-85218338679Q1Q1