Research Papers - Department of Electrical and Electronic Engineering

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    Estimating transformer parameters for partial discharge location
    (IEEE, 2014) Hettiwatte, S. N; Wang, Z. D; Crossley, P. A
    Partial discharge (PD) location in power transformers using electrical methods require transformer parameters to estimate the PD location. Previous research using a lumped parameter model of a transformer consisting of inductance (L), series capacitance (K) and shunt capacitance (C) has shown an algorithm for PD location. This algorithm does not require L, K and C values for the transformer in their explicit form. Rather, the products LC and LK are required. This paper presents three methods of estimating LC and LK values for a power transformer, which could then be used for PD location. The paper shows that all three methods give identical results confirming that either of these methods could be used for estimating LC and LK values. Results based on impedance measurements from two transformer windings are also presented.
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    Calculating a health index for power transformers using a subsystem-based GRNN approach
    (IEEE, 2017-11-07) Islam, M; Lee, G; Hettiwatte, S. N; Williams, K
    A power transformer is one of the most crucial items of equipment in the electricity supply chain. The reliability of this valuable asset is strongly dependent on the condition of its subsystems such as insulation, core, windings, bushings and tap changer. Integration of various measured parameters of these subsystems makes it possible to evaluate the overall health condition of an in-service transformer. This paper develops an artificially intelligent algorithm based on multiple general regression neural networks to combine the operating condition of various subsystems of a transformer to form a quantitative health index. The model is developed using a training set derived from four conditional boundaries based on IEEE standards, the literature and the knowledge of transformer experts. Performance of the proposed method is compared with expert classifications using a database of 345 power transformers. This shows that the proposed method is reliable and effective for condition assessment and is sensitive to poor condition of any single subsystem.