Defect Identification through Partial Discharge Analysis on HVDC: Partial Discharge Fingerprinting

The electricity grid spanning hundreds of thousands of kms is one of the most complex man-made network built in human history. Today, after a century of growth, progress and innovation, the electricity grid is in the process of undergoing another landmark shift in its operation. The introduction of renewable energy sources, especially, offshore wind connected to the load centres through >80 kms of underground subsea cable has caused a shift from AC transmission towards DC transmission. This is because AC cables suffer from high charging currents that reduce the useful current carrying capacity for long cables. On the contrary, High Voltage DC (HVDC) is acclaimed with higher current capacity for the same conductor dimension as AC and as a result the more sustainable alternative. Therefore, the infrastructure developed around the AC grid is now under pressure to adapt itself to the DC technology. This implies a dramatic change in a cascade of procedures and processes, beginning from designing new DC components, its testing and qualification, its validation and up to its commissioning, control and operation. Every step in the process is expected to be crucial and challenging given the newness of the technology and lack of experience.In the current scenario, this research rests itself in the testing and qualification phase of these DC components. The field of HV testing has not been exclusive tothis pressure to adapt and improvise its processes to accommodate the newest DC technological trends. New requirements are being defined to determine the quality of HVDC components and new methodologies developed to fulfil this. One of the most widespread test methodologies that has come to become a part of several tests such as factory acceptance tests (FATs), site acceptance test (SATs), routine tests and type tests is the measurement of Partial Discharge (PD). Partial discharge is a dielectric phenomenon that when measured is used as a proven marker for insulation quality. The inherent differences in the performance of the insulation under AC and DC operation have not allowed a direct adaption of the PD analysis techniques from AC to DC. This research will investigate the possibilities of defect identification through PD measurements under DC.With increasing HVDC installations such as GIS/GIL, cable links, convertors etc.,the method for its design validation and fitness through partial discharge measurement is gaining increasing popularity. This is only expected to rise with the introduction of renewable energy, electric vehicles (EVs) and its related infrastructure, lowered dependency on fossil fuels and an international policy shift towards the reduction of greenhouse gases. Moreover, given the remarkable success of partial discharge measurements in defect identification under AC, mounting expectations for a similar prospect under DC conditions is a thriving notion. Therefore, as a first steps towards characterizing PD defects under DC conditions this thesis studies the physics of discharge progression of 3 common defect types namely, corona, floating electrode and surface discharge in detail, in order to recognize minor if not major differences that will enable defect recognition. With this investigation, a comprehensive procedure is devised, enabling the identification of the three defects that were studied under DC conditions. The research also proposes the novel WePSA (Weighted Pulse Sequence Analysis) patterns discussed in chapter 7, section 7.2.2 as a prospective defect fingerprint that will allow identification of defects under DC.The simplicity and robust nature of these patterns make them self-explanatoryand easy to interpret. Several other unique defect behavioural features discovered during the study add value to this research and bring it closer to accomplishing the final goal of PD defect identification under DC stress conditions. This research could serve as a starting point for the scientific community to investigate further the other defect models and extend the defect discrimination strategy proposed in this thesis, chapter 7, section 7.5.