The performance and reliability of high voltage systems are critical for power generation and distribution, allowing power to continue flowing for everyday life.Partial discharge is both a cause and important indicator of damage developing within electrical insulation. By monitoring partial discharge activity during an electrical asset’s lifetime, an assessment of the insulation condition can be made and used to inform decisions about repairs or replacement.Most existing methods for partial discharge detection are only able to cover either a single device or short distance, requiring many discrete sensors for total coverage.Distributed acoustic sensing is already used widely in other commercial areas for geophysics and seismic data acquisition. However, it has been dismissed for detection of partial discharge due to low sample rates in comparison to the frequency of acoustic emissions from partial discharges.This thesis demonstrates through aliasing mechanisms, that detection of these high-frequency acoustic emissions can be downsampled and identified.This thesis reports report fibre-optic based distributed acoustic sensing for detection and measurement of partial discharge providing a continuous detection region of 5km with inherent positional information within 1.25m. The acoustic-strain interaction on the fibre optic, including the surrounding acoustic environment, is modelled demonstrating significant ringing due to reverberations of the initial impulse, as well as demonstrating an important aliasing method permitting the detection of much higher frequency signals than the original sampling rate.Laboratory partial discharge sources of both void and treeing varieties were manufactured and used to demonstrate this detection experimentally, covering a range of partial discharge sizes and sensor placements.This work also includes development of an alternative synchronisation method to allow for detailed sample-for-sample comparisons between different electrical, acoustic and distributed acoustic sensing measurements; each with different data types and sample rates.