Research Papers - Department of Electrical and Electronic Engineering

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    Application of the edcr algorithm in a cluster based multi-hop wireless sensor network
    (IEEE, 2006-10-18) Gamwarige, S; Kulasekere, E. C
    The energy driven cluster head rotation algorithm proposed in (Gamwarige, S and Kulasekere, E, 2005) and analyzed in (Gamwarige, S and Kulasekere, E, 2006) is based on a single hop communication model where the data messages from each cluster head (CH) node is sent directly to the base station (BS). As a result when the wireless sensor network (WSN) dimensions are large, the nodes located far away from the BS die much faster. In this paper a method of extending the lifetime of the WSN based on a multihop communication model applied to the EDCR algorithm is proposed. The modified EDCR (EDCR-MH) relays all CH to BS messages via other CH nodes by computing a shortest path based on local heuristic information. Further, the EDCR-MH also has provisions to minimize the burden due to excessive data relay on nodes closer to the BS. The results indicate that the proposed algorithm out performs algorithms like under similar conditions
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    PublicationOpen 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, G
    Accurate 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.