Pantograph-based electrical current transmission systems are used in electric traction vehicles. The contact surface between the pantograph and the catenary wire experiences mechanical and thermal effects during the train’s movement. Typically, this contact surface on the pantograph is covered by a segmented carbon or copper rod, attached to an aluminum base. Railways implement organizational measures for pantograph condition monitoring, based on scheduled inspections. Constitutionally, the option to replace contact elements or individual segments of the pantograph exists if wear is detected. Many scientific publications describe ideas for pantograph visualization and automated condition monitoring. These ideas are based on analyzing mechanical vibrations generated by the pantograph, acoustic vibration signal analysis, 3D geometric data of the pantograph surface captured by laser scanning, and combinations of several methods. However, in these publications, mechanical vibration analysis is limited to signal shape and spectral analysis. The possibility of treating the vibration signal as a random process using statistical methods has not been utilized. This study describes the possibility of evaluating classified mechanical pantograph vibrations using the signal’s autocorrelation transformation. A laboratory experiment confirmed the proposed method for evaluating informative signal classification features. The proposed method can distinguish between signals generated by a defective pantograph surface and identify different types of defects.

Households with photovoltaic installations contribute to reducing greenhouse gas emissions and mitigating global climate change. To fully utilize the benefits of clean solar energy, it is essential to ensure its efficient use, which can be achieved by consuming all generated energy locally, within the household or a microgrid community, eliminating wastage during the grid transportation and storage. In this work, we propose a method to enhance self-consumption by eliminating simultaneous bidirectional energy flow in the phase lines of a three-phase grid-tied household system, particularly in cases of significant load asymmetry. We developed an adaptive power flow management (APFM) algorithm which distributes solar-generated energy across the household grid’s phase lines based on their respective loads and solar power generation level. A simulation based on real-world data demonstrated that the APFM algorithm both eliminates simultaneous active power import and export flows and ensures that all power exported from the household to the DSO grid remains symmetrical across all phase lines. As observed from the simulation results, applying the APFM algorithm reduced the daily imported and exported energy between the household and the DSO grid by an average of 27.5% during the spring–autumn period for a specific household. Additionally, reducing energy flow led to a 5% average increase in self-consumption within the household grid, with peak improvements reaching 16.5%.