The electricity demand associated with charging electric vehicles (EVs) is significant and is rapidly expanding in terms of quantity, power capacity requirements, and waveform distortion. This growth is particularly critical regarding deploying EV charging infrastructure on the public low-voltage distribution networks, where the available short-circuit power may be limited, creating a risk of sensitive load coinciding within the same area of the grid. The current standards address these issues but predominantly focus on a certain frequency range; therefore, the high-frequency range needs extension and improvement. This article proposes a modeling technique for predicting the effects of conducted emissions (CEs) caused by a single-phase AC–DC converter for a grid-to-vehicle application within a single-phase system. This work includes a measurement design technique and exhibiting standard limits for conducted noise-separated signals into common mode and differential mode components while presenting a comprehensive investigation of the electromagnetic compatibility issues for a grid-connected, bidirectional converter operating at 4.4 kW and 18 kHz switching frequency. For the investigation, the converter is modelled in MATLAB/Simulink, and a statistical approach in Minitab software is also used to evaluate the circuit and frequency domain data. The developed model is also used to investigate the impact of a battery’s state of charge on CEs. The presented results show that the proposed emission standards are exceeded by a bidirectional converter under nonlinear load conditions.

In transmission systems, power transformers are key high-value components, and their consistent operation is fundamental for maintaining grid stability and achieving cost-effective performance. The Transformer Health Index (THI) integrates key diagnostic parameters including dissolved gas analysis, water content, oil quality indicators, and power factor providing essential insights for asset condition assessment and investment planning. In this study, THI prediction is conducted using the Random Forest algorithm, recognized in literature for its high predictive accuracy for transformer applications, in combination with data preprocessing and filtering techniques applied to transformer dataset. For the first time, to the best of our knowledge in the THI prediction literature, various wavelet families are systematically compared at the preprocessing stage to examine their influence on predictive accuracy. The results show that the Symlet-2 configuration consistently outperformed other families in both filtered and non-filtered datasets, while Coiflet-3 and Coiflet-5 achieved higher efficiency through dimensionality reduction but with an accuracy decrease of approximately 0.09–0.10 in R2 compared to Symlet-2. The findings demonstrate that the choice of wavelet family in the preprocessing phase directly impacts feature selection outcomes and model performance, offering valuable guidance for the development of high-accuracy transformer condition assessment frameworks.

Accurate state-of-charge (SoC) information is essential for the energy management of PEM fuel cell/battery hybrid electric vehicles (HEVs). This study examines how two SoC estimation approaches, Coulomb Counting (CC) and the Extended Kalman Filter (EKF), influence vehicle-level performance and internal power sharing under the same WLTP driving cycle and rule-based energy management strategy. Both methods yielded nearly identical vehicle-level performance, whereas the EKF produced a lower final SoC than CC (58.075% versus 60.902%) and greater SoC variability, indicating estimator-dependent differences in internal energy distribution. The findings are interpreted as estimator-dependent system behavior rather than a direct accuracy ranking.