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Voltage rise caused by high levels of distributed generation is manifesting as voltage regulation challenges for many electricity network service providers. In this environment it would be ideal to reduce supply voltage magnitudes, however, many network operators are hesitant to do so due to concerns related to consumer appliance performance at reduced supply voltage magnitudes. Voltage regulation requirements are defined by network standards and network service providers must ensure voltages remain within specified limits. Through an evaluation of domestic appliance performance when supplied at various voltage magnitudes, this paper examines the impact of varying voltage levels on residential appliances. Equipment energy demand, operation and actuation were monitored for each applied voltage magnitude. While no equipment failures were recorded, appliance behaviour varied significantly with applied voltage magnitude. Individual appliance conservation voltage reduction (CVR) factors have also been established. The results highlight the importance of good voltage regulation and provide substantiated appliance performance figures for future studies. The outcomes of this paper allow electricity network service providers to understand the implications of supply voltage magnitude on domestic appliance performance, whether it be understating of the impact of higher voltage magnitudes caused by distributed generation or implications of reducing voltage magnitudes to provide headroom for distributed generation integration. [ABSTRACT FROM AUTHOR]

This paper proposes a novel reactive power-based control strategy for single-phase PV inverters (PVIs) to simultaneously improve voltage unbalance (VU) and voltage regulation (VR) in low-voltage distribution networks. The proposed strategy relies on communication links between neighboring PVIs to exchange limited data. In this strategy, each PVI finds communication paths between itself and the closest neighboring ones connected to other phases. Then, using the obtained paths and the maximum and the minimum voltage magnitude of the grid, PVIs improve both VU and VR at the same time. The performance of the proposed control strategy is evaluated by various simulation studies using the IEEE European low-voltage test feeder and considering different operational conditions. In addition, the impacts of moving clouds and a failure in the communication links have been assessed. The simulation results exhibit that using the proposed control strategy, the voltage magnitude of all the nodes will remain within the allowed limits and at the same time, the phase voltage unbalance factor will be also significantly improved. [ABSTRACT FROM AUTHOR]

Operational performance in the low-voltage distribution network (LVDN) can be undermined by its inherent unbalances, which may become worse as the penetration of rooftop solar continuously increases. To address this issue, load balancing via phase-reconfiguration devices (PRDs), which can change phase positions of residential customers as required, provides a cost-efficient option. However, most reported approaches to control PRDs require that demands of all residential customers are available, which are not viable for many LVDNs without smart meters or advanced metering infrastructure (AMI) installed. To bridging the gap in this field, this paper proposes a novel method to control PRDs purely based on measurable data from PRDs, and its controller. Based on limited information, sensitivity analysis in the network with PRDs is studied, followed by the optimization model that comprehensively considers operational requirements in the network. Moreover, slack variables are introduced to the model, and penalized in the objective function to assure either a strategy that is secure or with minimized violations can always be provided. The model is a challenging mixed-integer non-convex programming (MINCP) problem, which is reformulated as an efficient solvable mixed-integer second-order cone programming (MISOCP) based on exact reformulations or accurate linear approximations. Simulations based on two modified IEEE systems, and a real system in Australia demonstrate that an efficient strategy can be provided to mitigate unbalances in the network. [ABSTRACT FROM AUTHOR]

With the advancements of the battery energy storage systems (BESSs), reduction of their manufacturing costs and government subsidies, the BESS uptake is likely to increase rapidly in power distribution networks. This paper investigates the effects of residential BESSs on low-voltage (LV) networks using the actual household load profiles equipped with BESS and solar-photovoltaic (PV) systems. The electricity consumption data collected via smart meters (2200 households with PV/BESS, 1950 households with PV only and 1000 households without a PV or a BESS) at different solar-PV penetration levels and network types are used to simulate real network operating scenarios. A real LV distribution network in Australia is analysed in DIgSILENT PowerFactory under different scenarios, such as, customers with and without a solar-PV/BESS, with a solar-PV but without a BESS, and without a solar-PV, by using both the power flow and quasi-dynamic simulation studies under balanced and the unbalanced network loading conditions. According to the study, customers experience large voltage excursions from solar-PV power exports, which could be resolved by the household BESS, provided that the BESS charging is coordinated with the solar-PV production. Moreover, quasi-dynamic simulation shows that the BESS could reduce the violation of the over-voltage limit during the solar peak hours (midday) by lowering the worst-case feeder voltage by 3%. Finally, extreme-event (high solar PV generation scenario) simulation shows that the implementation of the BESS controller to facilitate charging BESS during afternoon solar-PV export may reduce the negative grid impact and will assist to avoid network upgrades. [ABSTRACT FROM AUTHOR]

In Australia and many other countries, distribution network service providers (DNSPs) have an obligation to their customers to provide electrical power that is reliable and of high quality. Failure to do so may have significant implications ranging from financial penalties theoretically through to the loss of a license to distribute electricity. In order to ensure the reliability and quality of supply are met, DNSPs engage in monitoring and reporting practice. This paper provides an overview of a large long-running power-quality monitoring project that has involved most of Australia's DNSPs at one time or another. This paper describes the challenges associated with conducting the project as well as some of the important outcomes and lessons learned. A number of novel reporting techniques that have been developed as part of the monitoring project are also presented. A discussion about large-volume data management, and issues related to reporting requirements in future distribution networks is included. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel peak load management scheme for rural areas. The scheme transfers certain customers onto local nonembedded generators during peak load periods to alleviate network under voltage problems. This paper develops and presents this system by way of a case study in Central Queensland, Australia. A methodology is presented for determining the best location for the nonembedded generators as well as the number of generators required to alleviate network problems. A control algorithm to transfer and reconnect customers is developed to ensure that the network voltage profile remains within specification under all plausible load conditions. Finally, simulations are presented to show the performance of the system over a typical maximum daily load profile with large stochastic load variations. [ABSTRACT FROM AUTHOR]

Abstract-Harmonic monitoring has become an important tool for harmonic management in distribution system. A comprehensive harmonic monitoring program has been designed and implemented on a typical electrical medium-voltage distribution system in Australia. The monitoring program involved measurements of the three-phase harmonic currents and voltages from the residential, commercial, and industrial load sectors. Data over a three year period have been downloaded and available for analysis. The large amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. More sophisticated analysis methods are required to automatically determine which part of the measurement data are of importance. Based on this information, a closer inspection of smaller data sets can then be carried out to determine the reasons for its detection. In this paper, we classify the measurement data using unsupervised learning based on clustering techniques using the minimum message length technique, which can provide the engineers with a rapid, visually oriented method of evaluating the underlying operational information contained within the clusters. Supervised learning is then used to describe the generated clusters and to predict the occurrences of unusual clusters in future measurement data. [ABSTRACT FROM AUTHOR]

High-impedance faults are a challenging problem in power distribution systems. They often do not trigger protection devices and can result in serious hazards such as igniting fires when in contact with vegetation. The current research field dedicated to studying these faults is extensive but suffers from a constraining bottleneck of a lack of real experimental data. Many works set to detect and localize such faults rely on high-impedance fault low-fidelity models, and the lack of public data sets makes it impractical to have objective performance benchmarks. This letter describes and proposes a format for a data set of more than 900 vegetation high-impedance faults funded by the Victorian Government in Australia recorded in high-sampling resolution. The original data set is public, but it was made available through an obscure format that limits its accessibility. The presented format in this letter uses the standard hierarchical data format (HDF5), which makes it easily accessible in many languages such as MATLAB, Python, C++, and more. The data set compiler and visualizer script are also provided in the work repository1. [ABSTRACT FROM AUTHOR]

High impedance faults through contact with vegetation are one of the main causes of electrically caused wildfires. While detecting these faults is challenging on its own, it is important to do so in the context of the risk of vegetation ignition, as disconnecting the power infrastructure can have unwarranted, damaging consequences during an emergency. Hence, we propose a methodology for prevention of wildfires, through the accurate prediction and early detection of the ignition risk resulting from high impedance faults. Our methodology uses a set of features derived from time- and frequency-domain analyses. To test our methodology, we use a large, publicly available experimental dataset. Our results demonstrate that the methodology allows the detection of ignition risk well before its onset with high accuracy. [ABSTRACT FROM AUTHOR]

Analyzing the probability of power transformer failure is challenging because to observe a sufficient number of failures requires a fleet size larger than what is often operated by one utility. Parametric distributions have been applied to small datasets to model failure. However, an assumption is that the failure follows the chosen distribution. Reliability data from 97% of the 6057 utility-owned power transformers operating in mainland Australia and Tasmania were collected and analyzed to develop a more accurate understanding of failure statistics. Information on 564 failures and retirements occurring between 2000 and 2015 was collected, stratified into distribution, subtransmission, and transmission voltage class units, and then analyzed. The useful life of the three different populations was estimated, and the relationship between age and different failure types was investigated. [ABSTRACT FROM PUBLISHER]

Interruptions to supply and sags of distribution system voltage are the main aspects causing customer complaints. There is a need for analysis of supply reliability and voltage sag to relate system performance with network structure and equipment design parameters. This analysis can also give prediction of voltage dips, as well as relating traditional reliability and momentary outage measures to the properties of protection systems and to network impedances. Existing reliability analysis software often requires substantial training, lacks automated facilities, and suffers from data availability. Thus it requires time-consuming manual intervention for the study of large networks. A user-friendly sag and reliability assessment tool (SRAT) has been developed based on existing impedance data, protection characteristics, and a model of failure probability. The new features included in SRAT are a) efficient reliability and sag assessments for a radial network with limited loops, b) reliability evaluation associated with realistic protection and restoration schemes, c) inclusion of momentary outages in the same model as permanent outage evaluation, d) evaluation of the sag transfer through meshed subtransmission network, and e) simplified probability distribution model determined from available faults records. Examples of the application of the tools to an Australian distribution network are used to illustrate the application of this model. [ABSTRACT FROM AUTHOR]