Converter transformer fault reason identification method and system
A technology of fault cause and identification method, which is applied in the field of converter transformer fault cause identification method and system, which can solve the problems of restricting the training effect of the neural network diagnostic model, difficult identification, diagnosis of converter transformer, and lack of samples of equipment in abnormal state. , to achieve the effect of reducing operation risk, early warning of equipment abnormality, and improving operation and maintenance efficiency
Active Publication Date: 2020-09-11
GUANGZHOU BUREAU CSG EHV POWER TRANSMISSION
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Problems solved by technology
It is difficult to comprehensively identify and diagnose various faults of converters by relying on existing AC transformer diagnostic methods, and the lack of equipment samples in abnormal states restricts the training effect of diagnostic models based on neural networks
Method used
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Embodiment 1
[0050] refer to Figure 1-2 As shown, the converter transformer failure cause identification system provided in this embodiment mainly includes an on-site acquisition device, a wireless data processing unit, a wireless communication base station, and a server.
[0051] Among them, the on-site acquisition device includes an optical camera module, an oil chromatography gas acquisition module, an infrared thermal imager module, a temperature and humidity acquisition device, a leakage current acquisition module, a vibration acquisition module, and a noise acquisition module; The module is used to collect the readings of the SF6 pressure gauge and the oil conservator oil level gauge of the converter through the wall bushing, and recognize the gauge readings through the graphic recognition algorithm; the oil chromatography parameter acquisition module is used to collect the gas in the converter oil The infrared camera module is used to collect the temperature of the key parts of the...
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The invention discloses a converter transformer fault reason identification method and system. The method is used in a server, wherein a converter transformer historical fault data set and a normal data set are stored in the server, and the method comprises the following steps: S101, constructing a transformer fault diagnosis model based on a deep belief network and multi-dimensional information fusion by taking received multi-dimensional converter transformer online monitoring data as an input quantity of the transformer fault diagnosis model; S102, taking an output result of the transformerfault diagnosis model as one input of a Bayesian inference model to obtain a converter transformer defect type and a defect reason; and S103, outputting early warning equipment abnormal information according to the obtained converter transformer defect type and defect reason. The output result of the deep belief neural network is used as one input of the Bayesian inference model, so that the defect type and the defect reason of the converter transformer are deduced, and equipment abnormality is early warned in advance. The operation and maintenance efficiency is improved, and the operation risk of the converter transformer is reduced.
Description
technical field [0001] The invention relates to the technical field of direct current transmission, in particular to a method and system for identifying the cause of a converter transformer failure. Background technique [0002] DC transmission technology has developed rapidly in my country in recent years, and its advantages in long-distance power transmission, cross-regional networking, and flexible dispatching are becoming increasingly apparent. On-line monitoring technology of power equipment has been widely used in high-voltage and ultra-high-voltage direct current transmission systems. The online detection methods of converter station equipment are gradually enriched and improved. Through the application of online monitoring technology, the health status of the monitored equipment can be effectively monitored and comprehensively analyzed, and then the equipment defects and failures can be predicted and carried out in a planned way. Treatment and prevention, the online...
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Login to View More IPC IPC(8): G01R31/00G01D21/02G06N3/08G06N7/00
CPCG01R31/00G01D21/02G06N3/08G06N7/01
Inventor 周春阳洪乐洲石延辉石健谭明廖名洋曾海涛卢嵩李晨陈文超
Owner GUANGZHOU BUREAU CSG EHV POWER TRANSMISSION