Power transformers are designed to withstand the mechanical forces arising from various in-service events, such as over-voltage and lightning, which may cause deformation or displacement of winding. Among various techniques applied to power transformer fault diagnosis, frequency response analysis (FRA) can give an indication of winding deformation faults without expensive and interruptive operations of opening a transformer tank, which can minimise the impact on system operation and loss of supply to customers and consequently save millions of pounds in timely maintenance. However, in industrial practice, FRA is always used as a comparative method, by comparing a test frequency response with a reference set, which cannot provide an insight understanding of transformer internal faults. A range of research activities have been undertaken to utilise FRA in the development winding models but with limitations, such as too complicated models, large computation time and inaccurate responses in the high frequency range between 1MHz and 10MHz. The proposed research is to build on the experience already gained at Liverpool and to develop an accurate winding model and a reliable fault diagnosis approach. A new hybrid winding model will be developed by modifying the analytical approach and results of transformer winding analysis obtained by Rudenberg for each disc, and subsequently connecting the travelling wave equation of each disc in a form of Multi-conductor Transmission Line (MTL) model. This can significantly reduce the order of the model yet with good modelling accuracy in the high frequency range, which allows access to the current and voltage at any desired turns of a winding. The electrical parameters of the hybrid model will be estimated with the finite element method (FEM), and further identified with evolutionary algorithms based on actual FRA measurements. The characteristic signatures between particular winding faults and winding parameters will be derived, which can be employed to detect and distinguish winding deformation faults. Then, the simulation of the hybrid model will be used to extract high frequency fault fingerprints of FRA for improving the detection of small winding changes, which will be further examined and verified through laboratory studies. For typical winding fault diagnosis, both the quantitative and qualitative judgements are generally considered, which can be treated as evidence and are often incomplete and imprecise. The Evidential Reasoning (ER) algorithm is very suitable for combining such evidence with a firm mathematical foundation. In this project, an evidence-based fault diagnosis system will be constructed to aggregate diagnosis information and deal with uncertainties for reliable winding fault diagnosis. The work is to be carried out as a collaborative project between the University of Liverpool, OMICRON and NG, bringing together academic and industrial expertise in the field of transformer test, modelling and fault diagnosis. The outcome of the proposed research will be the new hybrid winding model and the evidence-based winding fault diagnosis system. The new approach aims to improve the fundamental understanding of multi-frequency signal propagation across a winding, which will allow extracting fault fingerprints in both the low and high frequency ranges and provide new diagnostic rules for early fault detection and location. The extracted high frequency fault fingerprints will provide a feasible solution for early fault detection, which can assist a FRA test kit manufacturer, e.g. OMICRON, in fully understanding FRA and improving test kit precision. The developed evidence-based system for winding fault diagnosis can be a useful decision support tool for utility companies, e.g. NG, for reliable fault diagnosis yet with high efficiency, when processing numerous FRA records.

In pursuit of Carbon net-zero, it is imperative to develop technologies that enhance the efficiency and reliability of energy conversion, e.g. in drivetrain and rapid chargers of electric vehicles (EVs). To put this into context, the larger battery size (i.e. 350 kWh at 800 V & 440 A for higher consumption) and long-range driving nature of heavy-duty EVs mandate ubiquitous access to extremely fast chargers at 350 kW for financially justifiable charging delays. These are proposed to directly connect to 11 kV feeders by high-frequency solid-state-transformers (SST), needing energy-dense fast power modules. Literature indicates that the emergence of wide-bandgap semiconductor devices, especially Silicon Carbide devices, enables us to deliver ultra-efficient reliable converters that deliver the next leap. Wide-bandgap power electronics is, however, currently being slowed down due to issues such as high dV/dt, common-mode interference and degradations. This means the full potential of wide-bandgap devices is still far from being obtained. The IEEE International Technology Roadmap for Wide-Bandgap Power Semiconductors (ITRW) has indicated that to unlock this potential, these limitations must be broken-through by 2028. As the UK is leading toward automotive electrification with a ban on the sale of new petrol & diesel engines by 2030, the UK needs to develop this technology locally, and earlier than this, to remain a global competitor in 'driving the electric revolution'. Research on SiC devices has shown that they are prone to progressive degradations, with a 'memory' effect that leads to a drift of electrothermal parameters away from the datasheet values. This can lead to failures in long-term operations. Nevertheless, it is demonstrated that under certain conditions the devices can recover to close to the initial state, if the devices are subjected to specific electrical and thermal conditions. This proposal, in a nutshell, aims to take advantage of these findings to explore ways of controlling and reversing degradation in devices using non-contact sensors which feed information to smart, active gate drivers, which, in turn, control the recovery of the power devices. To this end, this New Investigator Award project aims to make the power electronic core of these power converters responsive to operating conditions and functional degradations. This will be achieved by closing the loop between detection of change in SiC devices and how devices are controlled via their gates. This would permit SiC devices to be operated safely at higher switching speeds and thus efficiencies, than current datasheet limits allow. This is because datasheet nominal values are conservative in order to take every situation into account, whereas new situational awareness will allow these limits to be safely exceeded when appropriate. This is so important, particularly in the case of SiC power conversion, because whilst it is successfully taking over from silicon, it is also known that the potential performance of SiC is over an order higher than today's systems. Being able to safely break through these nominal limitations will reduce converter volume in cars and aircraft 2x or more, and bring a similar reduction in power loss in wind and solar power generation. Perhaps most importantly, it will reduce operational risk, by changing to safer driving modes as devices age or overheat. For example, this will reduce the cost of offshore wind power generation by generating more power at a lower risk of damage, and allow maintenance to be pre-empted. In the future, responsive power conversion with awareness of operating conditions and degradation could allow electric vehicles to detect the onset of drive failure, and activate a safe mode to get people home.