Research Papers - Department of Electrical and Electronic Engineering
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Publication Open Access A novel visualisation technique for dissolved gas analysis datasets–A case study(Australasian Universities Power Engineering Conference, 2013) Fonseka, H. A; Hettiwatte, S. N; Lee, GDissolved gas analysis is the most widely used diagnostic test in power transformers. There are established methods used in industry for interpreting DGA results. Among these are the IEEE Key Gas Method, Rogers’ Ratios and the Duval Triangle. However, collectively these methods can lead to conflicting results or unclassifiable measurements. This paper presents a visualization technique for interpreting DGA results to mitigate these effects, based on Kernel Principal Component Analysis. DGA measurements from more than 200 power transformers are used to validate the approach.Publication Embargo Missing measurement estimation of power transformers using a GRNN(IEEE, 2017-11-19) Islam, M. D. M; Hettiwatte, S. N; Lee, GMany industrial devices are monitored by measuring several attributes at a time. For electrical power transformers their condition can be monitored by measuring electrical characteristics such as frequency response and dissolved gas concentrations in insulating oil. These vectors can be processed to indicate the health of a transformer and predict its probability of failure. One weakness of this approach is that missing measurements render the vector incomplete and unusable. A solution is to estimate missing measurements using a General Regression Neural Network on the assumption that they are correlated with other measurements. If these missing values are completed, the entire vector of measurements can be used as an input to a pattern classifier. To test this approach, known values were deliberately omitted allowing an estimate to be compared with actual values. Tests show the method is able to accurately estimate missing values based on a finite set of complete observations.Publication Embargo Incipient fault diagnosis in power transformers by clustering and adapted KNN(IEEE, 2016-09-25) Islam, M. D. M; Lee, G; Hettiwatte, S. NDissolved Gas Analysis (DGA) is one of the proven methods for incipient fault diagnosis in power transformers. In this paper, a novel DGA method is proposed based on a clustering and cumulative voting technique to resolve the conflicts that take place in the Duval Triangles, Rogers' Ratios and IEC Ratios Method. Clustering technique groups the highly similar faults into a cluster and makes a virtual boundary between dissimilar data. The k-Nearest Neighbor (KNN) algorithm is used for indexing the three nearest neighbors from an unknown transformer data point and allows them to vote for single or multiple faults categories. The cumulative votes have been used to identify a transformer fault category. Performances of the proposed method have been compared with different established methods. The experimental classifications with both published and utility provided data show that the proposed method can significantly improve the incipient fault diagnosis accuracy in power transformers.Publication Open Access Application of Parzen Window estimation for incipient fault diagnosis in power transformers(The Institution of Engineering and Technology, 2018-12) Islam, M. D. M; Hettiwatte, S. N; Lee, GAccurate faults diagnosis in power transformers is important for utilities to schedule maintenance and minimises the operation cost. Dissolved gas analysis (DGA) is one of the proven and widely accepted tools for incipient fault diagnosis in power transformers. To improve the accuracy and solve the cases that cannot be classified using Rogers’ Ratios, IEC ratios and Duval triangles methods, a novel DGA technique based on Parzen window estimation have been presented in this study. The model uses the concentrations of five combustible hydrocarbon gases: methane, ethane, ethylene, acetylene and hydrogen to compute the probability of transformers fault categories. Performance of the proposed method has been evaluated against different conventional techniques and artificial intelligence-based approaches such as support vector machines, artificial neural networks, rough sets analysis and extreme learning machines for the same set of transformers. A comparison with other soft computing approaches shows that the proposed method is reliable and effective for incipient fault diagnosis in power transformers.Publication Open Access A nearest neighbour clustering approach for incipient fault diagnosis of power transformers(Springer Berlin Heidelberg, 2017-09) Islam, M. D. M; Hettiwatte, S. N; Lee, GDissolved gas analysis (DGA) is one of the popular and widely accepted methods for fault diagnosis in power transformers. This paper presents a novel DGA technique to improve the diagnosis accuracy of transformers by analysing the concentrations of five key gases produced in transformers. The proposed approach uses a clustering and cumulative voting technique to resolve the conflicts and deal with the cases that cannot be classified using Duval Triangles, Rogers’ Ratios and IEC Ratios Methods. Clustering techniques group the highly similar faults into a cluster providing a virtual boundary between dissimilar data. A cluster of data points may contain single or multiple types of faulty transformers’ data with different distinguishable percentages. The k-nearest neighbour (KNN) algorithm is used for indexing the three closest clusters from an unknown transformer data point and allows them to vote for single or multiple faults categories. The cumulative votes have been used to identify a transformer’s fault category. Performance of the proposed method has been compared with different conventional methods currently used such as Duval Triangles, Rogers’ Ratios and IEC Ratios Method along with published results using computational and machine learning techniques such as rough sets analysis, neural networks (NNs), support vector machines (SVMs), extreme learning machines (ELM) and fuzzy logic. The experimental comparison with both published and utility provided data show that the proposed method can significantly improve the incipient fault diagnosis accuracy in power transformers.Publication Embargo 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, KA 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.