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Abductive network model-based diagnosis system for power transformer incipient fault detection

Abductive network model-based diagnosis system for power transformer incipient fault detection

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An abductive network model (ANM)-based diagnosis system for power transformers fault detection is presented that enhances the diagnostic accuracy of the power transformer incipient fault. The ANM formulates the diagnosis problem into a hierarchical architecture with several layers of function nodes of simple low-order polynomials. The ANM links the complicated and numerical knowledge relationships of diverse dissolved gas contents in the transformer oil with their corresponding fault types. The proposed ANM has been tested on the Taipower company diagnostic records and compared with the previous fuzzy diagnosis system, artificial neural networks as well as with the conventional method. The test results confirm that the ANM possesses far superior diagnosis accuracy and requires less effort to develop.

Inspec keywords: power engineering computing; fault diagnosis; inference mechanisms; transformer oil; power transformer insulation; chemical analysis

Other keywords: power transformer incipient fault detection; transformer oil; dissolved gas contents; artificial neural networks; fuzzy diagnosis system; simple low-order polynomials; function nodes; hierarchical architecture; diagnostic accuracy; abductive network model-based diagnosis system; dissolved gas analysis

Subjects: Power engineering computing; Organic insulation; Transformers and reactors; Knowledge engineering techniques

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