Accurate identification of low-voltage (LV) network topology is becoming increasingly important, as reliable and detailed topological information is vital for effective network operation and precise modelling. Topology identification approaches based on smart-meter data typically rely on RMS voltage, current, and power measurements, which are limited in accuracy due to factors such as time resolution, measurement intervals, and instruments errors. This work introduces a novel methodology for distribution network topology identification through a multi-parametric analysis of smart-meter measurements. The core innovation lies in utilising the Calinski-Harabasz index (CH) as a weighting factor for multi-measurement distance matrices. The proposed framework integrates three distinct classes of measurements: Vrms, harmonic components (V2-V20), and THD. The methodology addresses critical challenges in measurement-based topology identification approaches, including high measurement errors, short data collection time intervals, and large time resolution. The resilience of the methodology stems from a hierarchical approach that combines correlation analysis, cluster validation, and graph-theoretic network reconstruction. The results demonstrate significant improvement in the accuracy and robustness of network topology identification, compared to approaches based on single-measurement types.

Identifying the topology of low-voltage (LV) networks is becoming increasingly important. Having precise and accurate topology information is crucial for future network operations and network modelling. Topology identification approaches based on smart-meter data typically rely on Root Mean Square (RMS) voltage, current, and power measurements, which are limited in accuracy due to factors such as time resolution, measurement intervals, and instrument errors. This paper presents a novel methodology for identifying distribution network topologies through the utilisation of smart-meter harmonic data. The methodology introduces, for the first time, the application of voltage Total Harmonic Distortion (THD) and individual harmonic components (V2–V20) as topology identifiers. The proposed approach leverages the unique properties of harmonic distortion to improve the accuracy of topology identification. This paper first analyses the influential factors affecting topology identification, establishing that harmonic distortion propagation patterns offer superior discrimination compared to RMS voltage. Through systematic investigation, the findings demonstrate the potential of harmonic-based analysis as a more effective alternative for topology identification in modern power distribution systems.

The development of solid-state devices has greatly increased the use of power electronic-based equipment. This equipment is nonlinear as when supplied with a sinusoid voltage the current waveform is non sinusoidal (i.e. contains harmonic components). Although these loads are nonlinear in the time-domain, however, they show linearity in the frequency-domain for a region around their operating point, which can be exploited. Moreover, in the frequency-domain the harmonic components exhibit phase-dependency. The tensor can represent this phase-dependency while complex numbers cannot, therefore analysing the electrical network using tensor representation rather than complex numbers is more accurate. Complex number can be shown to be a special case of a tensor. In the present work the application of tensors is for modelling the harmonics in a distribution system. Although time-domain programs, such as PSCAD/EMTDC, model accurately this phase-dependency the detail required, and computational cost make modelling a large distribution network unfeasible. Frequency-domain approach can model large networks efficiently, however, tensors rather than complex numbers must be used to capture the phase-dependency. Therefore, in the future, where the system is dominated by power electronic equipment, harmonic analysis software is better served using tensor representation rather than complex numbers. In order to use a tensor representation to solve for an electrical network a tensor is needed for each component in the system. The simulation accuracy is dependent on the tensor accuracy and over what range of distortion levels it is accurate. This paper introduces the use of tensor for harmonic analysis and looks at two methods for estimating the tensor for power electronic equipment. Namely Fourier Descriptors (FDs) and Average Admittances Locus (AAL). The evaluation is performed over a number of different distortion levels. An extensive set of simulations were performed using PSCAD/EMTDC to build a library of tensors for different nonlinear equipment. Laboratory tests were performed, also using different levels of voltage distortion, to verify the results obtained through simulation.

In light of the clean energy vision, the uptake of Electric Vehicles (EVs), DERs, and other nonlinear devices has been increased and has led to a growing issue of harmonic distortion in Low Voltage (LV) distribution networks. A key enabler to operate the system better is the use of measurement data, such as Smartmeter data. However, obtaining actual harmonic time-series measurements of LV distribution networks remains a challenge, limiting research efforts to investigate innovating algorithms that use this time-series data. This paper introduces a novel approach to generate realistic synthetic smartmeter data of LV distribution networks. The paper discusses various methods for obtaining time-series harmonic data. Furthermore, this paper briefly addresses the privacy considerations related to harmonics measurements.

Over the past few decades, there have been rapid advances in solid-state technology as well as a reduction in cost. This, coupled with the functionality and efficiency improvements they afford, has resulted in a massive increase in the use of electronic devices. Where traditionally, there were a few well-known nonlinear loads that needed to be considered, now there are numerous low-power devices. Although individually insignificant, collectively, they are very significant. This paper presents a tensor-based harmonic analysis approach that is capable of capturing important interactions while being computationally efficient enough to model a large distribution system. Numerical experiments are used to highlight the advantages of the tensor framework. Numerous papers have investigated the tensor parametrisation or its mathematical equivalent-harmonically coupled admittance matrices (also known as frequency coupling matrices). However, this paper, for the first time, demonstrates how these models can be applied to perform harmonic modelling of a complete low voltage (LV) distribution system.

Over the past few decades, there have been rapid advances in solid-state technology as well as a reduction in cost. This, coupled with the functionality and efficiency improvements they afford, has resulted in a massive increase in the use of electronic devices. Where traditionally, there were a few well-known nonlinear loads that needed to be considered, now there are numerous low-power devices. Although individually insignificant, collectively, they are very significant. This paper presents a tensor-based harmonic analysis approach that is capable of capturing important interactions while being computationally efficient enough to model a large distribution system. Numerical experiments are used to highlight the advantages of the tensor framework. Numerous papers have investigated the tensor parametrisation or its mathematical equivalent—harmonically coupled admittance matrices (also known as frequency coupling matrices). However, this paper, for the first time, demonstrates how these models can be applied to perform harmonic modelling of a complete low voltage (LV) distribution system.