  Rural electrification demands the use of inexpensive technologies such as
single wire earth return (SWER) networks. There is a steadily growing energy
demand from remote consumers, and the capacity of existing lines may become
inadequate soon. Furthermore, high impedance arcing faults (HIF) from SWER
lines can cause catastrophic bushfires such as the 2009 Black Saturday event.
As a solution, reliable remote electricity networks can be established through
breaking the existing systems down into microgrids, and existing SWER lines can
be utilised to interconnect those microgrids. The development of such reliable
networks with better energy demand management will rely on having an integrated
network-wide condition monitoring system. As the first contribution of this
thesis, a distributed online monitoring platform is developed that incorporates
power quality monitoring, real-time HIF identification and transient
classification in SWER network. Artificial Intelligence (AI) based techniques
are developed to classify faults and transients. The proposed approach
demonstrates higher HIF detection accuracy (98.67%) and reduced detection
latency (115.2 ms). Secondly, a remote consumer load identification methodology
is developed to detect the load type from its transients. An edge
computing-based architecture is proposed to facilitate the high-frequency
analysis for load identification. The proposed approach is evaluated in
real-time, and it achieves an average accuracy of 98% in identifying different
loads. Finally, a deep neural network-based energy disaggregation framework is
developed to separate the load specific energy usage from an aggregated signal.
The proposed framework is evaluated using a real-world data set. It improves
the signal aggregate error by 44% and mean aggregate error by 19% in comparison
with the state-of-the-art techniques.
