Abstract
Under the condition of long-term high-load operation, substation equipment is prone to oil leakage, which affects the operation safety of substation equipment and the stability of the power system. This paper proposes an oil leakage detection technology based on the fusion of simple linear iterative clustering (SLIC) and Transformer sub-station equipment, which is used to solve the problem of intelligent identification of oil leakage in oil-filled equipment such as transformers and transformers in substations. This paper first uses the SLIC method to segment the image to obtain superpixel of image data, and then uses the DBSCAN method based on linear iterative clustering to cluster similar superpixels. After training and learning, obtain the oil model with stable and accurate identification of oil leakage of substation oil-filled equipment. The experimental results show that the method proposed in this paper can efficiently identify oil leakage of substation equipment under the premise of ensuring stability, with an average recognition accuracy rate of 87.1%, which has high practicability and improves the detection and identification ability of oil leakage.
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Acknowledgements
This work was funded by the “Research on the key technology of intelligent annotation of power image based on image self-learning” program of the Big Data Center, State Grid Corporation of China.
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Chen, Z., Wang, L., Chen, S., Yu, J. (2023). The Fusion Oil Leakage Detection Model for Substation Oil-Filled Equipment. In: Zhai, G., Zhou, J., Yang, H., Yang, X., An, P., Wang, J. (eds) Digital Multimedia Communications. IFTC 2022. Communications in Computer and Information Science, vol 1766. Springer, Singapore. https://doi.org/10.1007/978-981-99-0856-1_5
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