A transformer component identification method based on a YOLO network model

Abstract

The invention belongs to the technical field of transformer detection, and discloses a YOLO network model-based transformer component identification method, which comprises the following steps: acquiring a monitoring image, and carrying out denoising and size scaling preprocessing; calibrating components of the transformer in the image; carrying out clustering on the data set to generate an appropriate priori frame scale; inputting the calibrated image into a deep neural network model for training and continuously adjusting parameters, so that the network model can automatically detect and position main components of the transformer, has a classification capability, and identifies which class the main components of the transformer belong to; carrying out detection positioning and state recognition by using the trained model; According to the invention, main parts of the transformer equipment in the transformer substation monitoring image can be automatically detected and identified; compared with the prior art, the method has the advantages that the method is high in recognition accuracy, the model uses a fine-grained feature structure, that is, depth features are fused, differentfeature maps adapt to targets of different scales, and a good recognition effect on small target detection is achieved.

Description

technical field [0001] The invention belongs to the technical field of transformer detection, and more specifically relates to a method for identifying transformer components based on a YOLO (You Only LookOnce) network model. Background technique [0002] With the continuous development of the economy, the power consumption has increased sharply, the scale of the power grid has become larger and larger, and the power system has transformed into a large-capacity, ultra-high voltage, and automation direction. Transformers in substations are widely used in power systems, and their safety plays a key role in power transmission and distribution. Whether the transformer can operate normally is directly related to the stability and safety of the entire power system. If the transformer fails, the operation status of the power system will be seriously affected, and even lead to the paralysis of the entire power system. How to improve the safety and stability of the transformer and e...

AI Technical Summary

Problems solved by technology

[0004] In view of the above defects or improvement needs of the prior art, the present invention provides a transformer component recognition method based on the YOLO network model, the purpose of which is to solve the problem of poor detection effect of traditional detection methods on small targets through multi-scale detection

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