Partial Discharge Phenomena in Converter and Traction Transformers: Identification and Reliability

After the development of power electronics converters, the number of transformers subjected to non-sinusoidal stresses (including DC) has increased in applications such as HVDC links and traction (electric train power cars). The effects of non-sinusoidal voltages on transformer insulation have been investigated by many researchers, but still now, there are some issues that must be understood. Some of those issues are tackled in this Thesis, studying PD phenomena behavior in Kraft paper, pressboard and mineral oil at different voltage conditions like AC, DC, AC+DC, notched AC and square waveforms. From the point of view of converter transformers, it was found that the combined effect of AC and DC voltages produces higher stresses in the pressboard that those that are present under pure DC voltages. The electrical conductivity of the dielectric systems in DC and AC+DC conditions has demonstrated to be a critical parameter, so, its measurement and analysis was also taken into account during all the experiments. Regarding notched voltages, the RMS reduction caused by notches (depending on firing and overlap angles) seems to increase the PDIV. However, the experimental results show that once PD activity has incepted, the notches increase PD repetition rate and magnitude, producing a higher degradation rate of paper. On the other hand, the reduction of mineral oil stocks, their relatively low flash point as well as environmental issues, are factors that are pushing towards the use of esters as transformer insulating fluids. This PhD Thesis also covers the study of two different esters with the scope to validate their use in traction transformers. Mineral oil was used as benchmark. The complete set of dielectric tests performed in the three fluids, show that esters behave better than mineral oil in practically all the investigated conditions, so, their application in traction transformers is possible and encouraged.

Power flow forecasting and smart charging for electric vehicle charging systems with photovoltaic integration

An essential role in the global energy transition is attributed to Electric Vehicles (EVs) the energy for EV traction can be generated by renewable energy sources (RES), also at a local level through distributed power plants, such as photovoltaic (PV) systems. However, EV integration with electrical systems might not be straightforward. The intermittent RES, combined with the high and uncontrolled aggregate EV charging, require an evolution toward new planning and paradigms of energy systems. In this context, this work aims to provide a practical solution for EV charging integration in electrical systems with RES. A method for predicting the power required by an EV fleet at the charging hub (CH) is developed in this thesis. The proposed forecasting method considers the main parameters on which charging demand depends. The results of the EV charging forecasting method are deeply analyzed under different scenarios. To reduce the EV load intermittency, methods for managing the charging power of EVs are proposed. The main target was to provide Charging Management Systems (CMS) that modulate EV charging to optimize specific performance indicators such as system self-consumption, peak load reduction, and PV exploitation. Controlling the EV charging power to achieve specific optimization goals is also known as Smart Charging (SC). The proposed techniques are applied to real-world scenarios demonstrating performance improvements in using SC strategies. A viable alternative to maximize integration with intermittent RES generation is the integration of energy storage. Battery Energy Storage Systems (BESS) may be a buffer between peak load and RES production. A sizing algorithm for PV+BESS integration in EV charging hubs is provided. The sizing optimization aims to optimize the system's energy and economic performance. The results provide an overview of the optimal size that the PV+BESS plant should have to improve whole system performance in different scenarios.