Abstract In the offshore oil platform power system, the replacement of electric equipment is very inconvenient due to the far distance between the platform and the land. Transformer is one of the most important electric equipment in the oil power system, whose reliable operation is of essential importance to the normal oil exploitation. Therefore, the condition of the transformer should be monitored continuously to find out the possible operating abnormality as soon as possible. The transformer temperature is a good indicator to reflect the transformer operating condition. Moreover, there is a large amount of historical operating data provided by the monitoring system in the oil platform power system, which can be used as the basis to distinguish the normal and abnormal operating condition of the transformer. In this paper, an abnormal temperature pre-warning method is proposed for the dry-type transformer in the oil platform power system. Based on the historical operating data, the dry-type transformer temperature model is established by the Sparse Bayesian Learning. The proposed model provides a temperature warning range. By entering the current transformer operating parameters into the model, the temperature range for a normal operating state can be obtained. If the temperature measured by the transformer sensors exceed the expected ranges, an abnormality may occur. The residual statistical analysis is adopted to distinguish the measurement errors and actual transformer abnormal operations. The effectiveness and validity of the proposed method are verified based on the real field data of an oil platform transformer.

中文翻译：

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基于温度的海上石油平台干式变压器故障预警方法

摘要 在海上石油平台电力系统中，由于平台与陆地距离较远，电力设备的更换十分不便。变压器是石油电力系统中最重要的电气设备之一，其可靠运行对石油的正常开采至关重要。因此，应持续监测变压器的状况，以尽早发现可能的运行异常。变压器温度是反映变压器运行状况的良好指标。而且，石油平台电力系统中有大量监控系统提供的历史运行数据，可以作为区分变压器正常和异常运行状态的依据。在本文中，提出了石油平台电力系统干式变压器异常温度预警方法。基于历史运行数据，通过稀疏贝叶斯学习建立干式变压器温度模型。所提出的模型提供了温度警告范围。通过将电流互感器运行参数输入模型，可以获得正常运行状态的温度范围。如果变压器传感器测量的温度超出预期范围，可能会出现异常。采用残差统计分析，区分测量误差和实际变压器异常运行。基于某石油平台变压器的真实现场数据，验证了所提方法的有效性和有效性。