Online Fault Gas Monitoring System for Hermetically Sealed Power Transformers

Azirani, Mohammad Akbari; Kuhnke, Moritz; Werle, Peter; Sorgatz, W.

doi:10.1109/cmd.2018.8535777

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…Due to the special installation and operation environment of oil chromatography sensor, some researchers have been conducted to improve the reliability of the sensor by developing new oil chromatography sensor and designing distributed sensor technology. Most of the studies are based on offline experimental methods to evaluate the monitoring devices, and the focus of the studies is on the errors caused by incomplete degassing, oil fouling or oil seepage in the monitoring devices, and the studies are partly on the oil chromatography sensors [16]. However, for the actual operating oil chromatography sensors, the previous research method requires taking the sensors out of operation and conducting offline experiments, which is tedious and has a long evaluation period, and it is very difficult to evaluate a large number of sensors in a regional power grid.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Oil Chromatography Sensor Failure for Transformer Based on Multi-dimensional Data-driven

Sha,

He,

Zhang

2023

J. Phys.: Conf. Ser.

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Online monitoring and analysis of transformer oil chromatography is of great significance for the assessment of internal insulation with transformer operation. The existence of numerous invalid monitoring sensors in the field makes the quality of online monitoring data degraded, which makes the online monitoring system unable to monitor the operating status of transformers in a timely and accurate manner. This paper propose a data-driven method for evaluating the effectiveness of oil chromatography monitoring sensors by selecting a fixed-length feature data set from online data for evaluation, and then using the distribution of abnormal values, the distribution of consecutive identical values, the variation of coefficient of variation and the variation of gas production rate in the feature data set as criteria to judge the feature data set and obtain the corresponding discriminant values. Afterwards, the discriminant values are assigned weights according to the focus of each criterion to obtain the sensor status values, which are compared with the preset tolerance level to obtain the sensor evaluation results. The method proposed evaluates the effectiveness of online monitoring sensors from several aspects, which can detect faulty sensors in time and is of great significance for improving the accuracy of online monitoring data and the reliability of monitoring device.

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Section: Introductionmentioning

confidence: 99%