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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]

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]

Nonlinear preprocessors, including clipping, blanking, and joint blanking/clipping have been proposed to be employed in orthogonal frequency-division multiplexing systems for mitigating the impulsive noise in power-line communications. In this paper, we propose using a new variant of nonlinear preprocessor, namely, deep clipping, to mitigate the impulsive noise. In contrast to clipping, blanking, and joint blanking/clipping, where one or two thresholds is used to detect if the impulsive noise occurs, and if the impulsive noise occurs how to handle it, deep clipping is characterized by two alternative parameters, namely, a threshold and a depth factor. By optimizing the two parameters, we find that deep clipping achieves better signal-to-noise ratio (SNR) performance than clipping, blanking, and joint blanking/clipping. In addition, we present a mathematical expression for deep clipping and show that the SNR expressions regarding conventional clipping, blanking, and joint blanking/clipping can be easily obtained by using our analytical methods. [ABSTRACT FROM AUTHOR]

High-impedance faults (HIFs) are linked to enduring unaddressed knowledge gaps due to their diverse and complex behavior, despite being extensively researched disturbances. Vegetation HIFs, for instance, are a particular type of fault that can lead to great fire hazards and life risks. They have unique fault signatures and should receive special attention if fire risk mitigation is desired. This paper focuses on the detection of these distinct, very small current faults. As the main correlational features, the proposed methodology uses the vegetation fault signatures’ high-frequency content. Different from many previous works that rely on HIF models, the approach validation is performed using a real dataset comprising a large number of experiments, sampled in a functioning network in the presence of noise. The classification is performed by boosted decision trees, which showed high dependability and security in the classification of small phase-to-earth and phase-to-phase HIFs. [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]