Search

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]

A high penetration of rooftop solar photovoltaic (PV) resources into low-voltage (LV) distribution networks creates reverse power-flow and voltage-rise problems. This generally occurs when the generation from PV resources substantially exceeds the load demand during high insolation period. This paper has investigated the solar PV impacts and developed a mitigation strategy by an effective use of distributed energy storage systems integrated with solar PV units in LV networks. The storage is used to consume surplus solar PV power locally during PV peak, and the stored energy is utilized in the evening for the peak-load support. A charging/discharging control strategy is developed taking into account the current state of charge (SoC) of the storage and the intended length of charging/discharging period to effectively utilize the available capacity of the storage. The proposed strategy can also mitigate the impact of sudden changes in PV output, due to unstable weather conditions, by putting the storage into a short-term discharge mode. The charging rate is adjusted dynamically to recover the charge drained during the short-term discharge to ensure that the level of SoC is as close to the desired SoC as possible. A comprehensive battery model is used to capture the realistic behavior of the distributed energy storage units in a distribution feeder. The proposed PV impact mitigation strategy is tested on a practical distribution network in Australia and validated through simulations. [ABSTRACT FROM PUBLISHER]

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]