Transformer fault combined diagnosis model building method and diagnosis method

A transformer fault diagnosis model technology, applied in the field of transformers, can solve problems such as difficulty in popularizing transformer fault diagnosis methods, scarcity of fault samples, and insufficient consideration of the advantages and disadvantages of diagnostic models in diagnostic methods

Inactive Publication Date: 2018-05-18
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

[0002] Fault diagnosis of transformer based on dissolved gas analysis (DGA) in oil is simple and feasible, and has been verified in actual operation, maintenance and a large number of research results, but the problem is that fault samples are scarce, which is based on DGA and samp

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  • Transformer fault combined diagnosis model building method and diagnosis method
  • Transformer fault combined diagnosis model building method and diagnosis method
  • Transformer fault combined diagnosis model building method and diagnosis method

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Embodiment Construction

[0031] The combined diagnostic model includes two parts, the primary diagnostic model and the secondary diagnostic model. From the process, it contains training process and diagnosis process. like figure 1 As shown, the primary sample data formed by the original data is input to the Naive Bayesian classifier, the RVM fault diagnosis model, the diagnosis model based on the physical meta-theory and the cloud model, and these models are trained; the diagnosis results of the diagnosis model group form a secondary The secondary data is input into the RVM fault diagnosis model to train the model. After the training, the measured oil chromatogram data is input into the naive Bayesian classifier, the RVM fault diagnosis model, the diagnosis model based on the physical element theory and the cloud model for the initial diagnosis, and the diagnosis results of the diagnosis model group form the secondary data, through The RVM fault diagnosis model performs secondary diagnosis, and fina...

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Abstract

The invention provides a novel power transformer fault combined diagnosis model building method and a fault diagnosis method; the diagnosis model building method comprises the following steps: using at least two of the following diagnosis models to primarily diagnose the oil dissolving gas analysis data: a Naive Bayes diagnosis model, a RVM diagnosis model and a matter-element diagnosis model; carrying out weighted average for the primary diagnosis result, using the secondary RVM diagnosis model to make the secondary diagnosis, thus obtaining the combined diagnosis model; using the combined diagnosis model to make fault diagnosis. The method can fully utilize advantages of various diagnosis models, thus effectively improving the diagnosis precision and validity.

Description

technical field [0001] The invention belongs to the field of transformers, and in particular relates to a power transformer fault combination diagnosis method. Background technique [0002] Fault diagnosis of transformer based on dissolved gas analysis (DGA) in oil is simple and feasible, and has been verified in actual operation, maintenance and a large number of research results, but the problem is that fault samples are scarce, which is based on DGA and sample The generalization of the learned transformer fault diagnosis method poses difficulties. According to the research results of various existing intelligent diagnosis methods, each method has different advantages and disadvantages. In view of this, looking for a method that can give full play to the advantages of multiple intelligent diagnostic methods, [0003] Matter-element theory can deal with qualitative and quantitative problems well, and has the advantages of simple modeling and good effect, while cloud model...

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Application Information

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IPC IPC(8): G01R31/00G06K9/62
CPCG01R31/00G06F18/24G06F18/29
Inventor 王艳朱永利
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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