Using improved self-organizing map for partial discharge diagnosis of large turbogenerators

Partial discharge (PD) classification is a powerful way to predict insulation problems of the windings of rotating machinery. While online PD tests have been carried out for over 40 years, effective diagnostic methods are still under development. In this paper, a practical diagnostic method is proposed based on an improved self-organizing map (SOM) with improved clustering indications. Three feature extraction methods are employed for the SOM implementation, including Weibull analysis, statistical operators, and fractal parameters. Experimental PD data of industrial model bars are used to validate the efficiency of using SOM for PD classification. The method is applied to investigate the turbine generator analyzer (TGA) data obtained from a power plant of British Nuclear Fuels Ltd. Diagnostic results are included to demonstrate that the relationship between the new PD measurement and historical data can be visualized and more confidential diagnostic information can be provided, especially when small-size database, new class of data, and doubtful measurements are involved in a practical environment.