Pattern Recognition for Continuous Partial Discharge Measurements at Power Transformers

电力变压器连续局部放电测量的模式识别

• 批准号: 317310948
• 负责人: Professor Dr.-Ing. Stefan Tenbohlen
• 金额: --
• 依托单位: Institut für Energieübertragung und Hochspannungstechnik (IEH)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/317310948?language=en
• 关键词: Pattern; Recognition; Continuous; Partial; Discharge

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.

在德国使用的许多变压器正接近其预期使用寿命的终点。为了确保尽可能长时间的安全运行并防止故障，必须监测绝缘系统的状况。局部放电测量是绝缘系统状态评估的重要现场测量方法。然而，通过监测系统对PD的连续测量产生大量数据，这些数据需要自动处理和分析。为了分析大量的PD数据并进行状态评估，本研究项目将研究模式识别和机器学习方法。在实验室和现场对不同的局部放电源进行测量后，对产生的三维相位分辨图案进行分析，提取区别性特征并建模。然后，这些特征通过分类算法分配给特定类别的故障。气体绝缘开关设备（GIS）和发电机中的局部放电的分类已经有了一些方法。在本研究项目中，还将研究这些方法是否以及如何也可以用于电力变压器中的局部放电诊断。与GIS不同，几个PD源通常同时活动，并通过变压器中的外部放电叠加更多的噪声。为了评估叠加的PD信号，必须首先将它们分离。为此，必须探索适当的方法。由于三维图案可以被认为是具有二维和颜色值的图像，除了已知的分类过程之外，还将检查图像处理方法的使用以用于PD诊断。近年来图像处理领域的巨大进步将被应用于PD的诊断。因此，应连续自动评估PD活性。因此，可以在早期阶段预测绝缘的故障，并且可以防止Transformer的故障。