Australian Power Quality & Reliability Centre

Established 1996

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Vic Gosbell Photo

Emeritus Professor Vic Gosbell

Technical Advisor to Australian Power Quality &, Reliabiity Centre and Emeritus Professor

School of Electrical, Computer and Telecommunications Engineering

Tel: +61 2 4221 3402

Fax: +61 2 4221 3236



Emeritus Professor Gosbell was a cadet engineer with Sydney County Council while an undergraduate student. He obtained his Ph.D. in 1971 from the University of Sydney with work on the asynchronous operation of turbogenerators.

In 1972 he commenced lecturing at the University of Sydney where his research interests included model power systems, power system stability, HVDC transmission, power electronics and variable speed motor drives. In 1990 he moved to the University of Wollongong where he became foundation Professor of Power Engineering. His current research interest is power quality with an emphasis on harmonics, PQ survey measurements, and standards.

He is a member of the Standards Australia "Power Quality" Committee, a Fellow of the Institution of Engineers, Australia and past Chairperson of the Australasian Committee for Power Engineering. He was the recipient of the M.A. Sargent Medal in 2008.

Research Interests

  • Harmonic Behaviour of Power System Loads

    There is an increasing use of power electronics in lighting, electric motor drives and many other areas for greater efficiency and accuracy of control. These circuits all involve the use of switching electronics and draw non-sinusoidal current from the supply, distorting the supply voltage waveform. This project will determine means to estimate the harmonic current drawn by non-linear loads under different conditions.

  • Development of harmonic connection agreements for MV and LV customers

    The harmonic voltage levels in a power system are a combination of the effects from all large distorting loads. When the level of harmonic voltage distortion is excessive, there needs to be clear procedures for identifying which load is the major cause so that appropriate mitigation can be taken. Techniques need to be developed to determine a fair and reasonable allocation for each load as a basis for connection agreements between utility and customer. Present harmonic standards contain procedures which are applicable when all customers are connected at the transformer output. This is inadequate for MV and LV customers who are connected part-way along feeders and distributors which have significant changes in fault level along their length. The work aims to develop methods for allowing for customers connected at points of varying fault level using models requiring data which is easy to obtain.

  • Detecting harmonic sources

    When harmonic voltages exceed maximum acceptable limits, customers are not entirely responsible for their level of distorting current. Situations can arise in which customers who are normally compliant find that their current exceeds what has been specified in a connection agreement. Practicable methods need to be developed which will allow a clear demonstration as to which customer, if any, is exceeding their allowance. Methods being investigated include harmonic power and reactive power flow and observing the natural daily variation of harmonics voltage and current levels. Various analysis and simulation tools are being investigated for their suitability for this project.

  • Power quality surveying methodologies

    Power system companies are being required to given undertakings regarding the level of PQ disturbances at different voltage levels within their network. These undertakings can only be given with confidence if the utility has a process for "building" the PQ limits into the network at the planning stage. Measurement of PQ levels at a sample of sites is required to prove that the planning processes are adequate. PQ monitors, a communications network and suitable PQ database and reporting facilities are very expensive. Research is required to determine the optimum placement of PQ monitors so that the best use is made of a limited budget. A starting point for this is the development of methods of estimating sources of PQ disturbances and their propagation.

  • Power quality reporting

    The data from a single PQ monitor is very large. For example, consider harmonics which require 3 readings for each of 39 harmonics and the THD every 10 minutes. Over a year, assuming 4 bytes/reading, gives about 25MB/site. Detailed examination of these readings for insights would be tedious if each 10 minute reading has to be considered manually. Instead there need to be summary values determined which can be assessed quickly and not overlook important details. Research is being applied to developing summary figures applied to the original data in several steps. A framework has been proposed in which the data is considered to be in several layers, with each layer being a summary of the one underneath it. Hence, if one summary figure is excessive, the network planner can be guided into looking at the detail in the next level down. Key concepts developed for the discrete disturbances are the severity index for characterizing a multi-parameter discrete event, and the combination of severity indices into a disturbance index. For continuous disturbances, multi-parameter disturbances such as harmonics and flicker give rise to a single parameter via the two step process of normalization and consolidation. Having obtained a single index for each disturbance type, they can be combined into a single Unified PQ Index for a site.

  • Power quality data analysis

    The mass of data gathered for a sample of sites for a utility survey has the potential to reveal the good and bad influences on power quality if an appropriate diagnostic procedure can be determined. Several functions have been identified and it now requires the details of analysis procedures to be developed.

    1. The comparison of disturbance levels with reduced targets in the case of lightly loaded networks allows network problems to be found that would otherwise not be revealed until the network is loaded.
    2. Analysis of long term trends in PQ levels might reveal potential problems in time for inexpensive corrective action to be taken.
    3. Factor analysis enables the factors which contribute to good and bad PQ levels to be identified and considered in PQ planning.
    4. Data mining allows insights to be found without having to postulate the types of input/output models required for factor analysis.

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