Rapid evolution of electricity distribution networks challenges the maintenance of up-to-date information in electricity utility databases. This hinders the ability of utilities to understand phase connectivity and topology of users in their distribution networks. Extensive research has been conducted to develop smart meter data-driven phase identification and topology determination approaches as alternatives to the conventional, time-consuming, and expensive approach of manual inspection. However, the majority of such approaches are challenged by low levels of smart meter penetration in distribution networks, entailing further investigation. The objective of this paper is to contribute to this challenge by proposing an alternative smart meter data-driven approach of user phase identification, verification, and topology determination and testing the method on a real Australian distribution network under varied levels of smart meter penetration. This paper first presents a smart meter data-driven user phase identification tool using k-means clustering. Then, a smart meter data-driven user phase verification and topology determination approach is introduced by analyzing voltage-to-power sensitivities obtained from linear regression. Four distinct linear regression models are developed and compared to recognize relevant parameters and input variables leading to the most reliable sensitivities. The overall process proposed in this study demonstrated high accuracy at original smart meter penetration of 75% of the case study DN. The performance at reduced smart meter penetrations of 50% and 25% is also examined and discussed in the paper.

The widespread integration of rooftop solar photovoltaic systems into electricity distribution networks often leads to poor voltage regulation at user connection points, potentially breaching system voltage standards. Therefore, it is important for distribution network service providers to thoroughly assess such real and potential impacts to ensure compliant and safe operation of their power systems. The conventional approach of non-linear power flow-based voltage estimation using model-based methods is complex and time-intensive. Consequently, there is an increasing research interest towards model-free voltage estimation methods as a reliable alternative. This paper proposes and compares two distinct model-free voltage estimation approaches that can be utilised for effective hosting capacity estimation of residential solar photovoltaic systems in low voltage distribution networks. One approach utilises linear regression based on linearised power flow equations, while the other employs neural networks to capture non-linear power flow dynamics. The study developed 16 linear regression models and 648 neural network models utilising historical data collected from residential smart electricity metres in a real low voltage distribution network and compared their efficacy against conventional model-based, non-linear power flow simulations. Results indicate that the proposed model-free voltage estimation approaches can estimate voltages at user connection points in a similarly accurate but faster manner compared to the model-based approach. Observations show that the proposed linear regression-based voltage estimation approach is superior to the proposed neural network-based voltage estimation approach in terms of interpretability and practicality.