Pattern Recognition for Continuous Partial Discharge Measurements at Power Transformers

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Many of the transformers used in Germany are approaching the end of their projected service life. To ensure safe operation as long as possible and to prevent failure, the condition of the insulation system has to be monitored. The measurement of partial discharges (PD) is an important on-site measuring method for condition assessment of the insulation system. However, the continuous measurement of the PDs by means of monitoring systems generates large amounts of data, which need to be automatically processed and analyzed.In order to analyze the extensive PD data and enable condition evaluation, methods of pattern recognition and machine learning are to be examined in this research project. After the measurement of different PD sources in the laboratory and on-site, the resulting three-dimensional phase resolved patterns are analyzed, distinctive features are extracted and modeled. These characteristics are then assigned by means of a classification algorithm to a specific class of fault. Some methods are already known for the classification of partial discharges in gas-insulated switchgear (GIS) and generators. In this research project it will also be investigated whether and how these methods can also be used for partial discharge diagnosis in power transformers. Unlike GIS, several PD sources are often active simultaneously and superimposed with more noise by external discharges in transformers. In order to evaluate the superimposed PD signals, they must first be separated. For this, appropriate methods have to be explored. Since three-dimensional patterns can be regarded as images with two dimensions and a color value, in addition to the classification procedures already known, the use of image processing methods are to be examined for the PD diagnosis. The great advances in the field of image processing in recent years will be applied to the PD diagnosis. The PD activity should thereby be evaluated continuously and automatically. Thus the failure of the insulation can be predicted at an early stage and a failure of the transformer can be prevented.