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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiong Hao Chang Tao Liao Rui-jing Li Jian Sun Cai-xin |
| Copyright Year | 2006 |
| Description | Author affiliation: Key Lab. of High Voltage & Electr. New Technol. of Minist. of Educ., Chongqing Univ., Chongqing (Xiong Hao; Chang Tao; Liao Rui-jing; Li Jian; Sun Cai-xin) |
| Abstract | Dissolved gas analysis (DGA) of power transformer oil is an important technique to detect the incipient faults. Recently various artificial intelligence methods have been developed to interpret DGA results such as artificial neural networks (ANNs), expert system and clustering analysis. Against the deficiencies associated with the constrained memberships used in original fuzzy c-means clustering algorithm, the possibilistic c-means clustering algorithm is introduced. Its memberships may be interpreted as degrees of possibility of the samples belonging to the classes. Furthermore, the kernel-based learning method can nonlinear map samples in the original low- dimensional space to a high-dimensional feature space. Then the useful features can be effectively exacted and enlarged for improving the accuracy of clustering. Therefore, a kernel-based possibilistic c-means clustering algorithm is proposed in this paper. The new algorithm is used to analyze DGA data in power transformer. Simulation results are given to illustrate that this algorithm is accurate in clustering and is fast in convergence speed, and it is highly robust in noisy environments. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 5324583 |
| Page Count | 5 |
| File Format | |
| ISBN | 1424401100 |
| DOI | 10.1109/ICPST.2006.321491 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-10-22 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fault diagnosis Power transformers Clustering algorithms Dissolved gas analysis Petroleum Fault detection Artificial intelligence Artificial neural networks Diagnostic expert systems Learning systems fault diagnosis Power transformer Dissolved Gas Analysis (DGA) kernel function possibilistic c-means clustering |
| Content Type | Text |
| Resource Type | Article |
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