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Article type: Research Article
Authors: Sun, Xianshan; * | Sheng, Yuefeng | Mao, Hongfei | Qian, Qingfeng | Cai, Qingnan
Affiliations: Control Sub Center, Deqing County Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd, Deqing, China
Correspondence: [*] Corresponding author. Xianshan Sun. E-mail: [email protected].
Abstract: In order to solve the problems of tedious, insufficient manpower, low efficiency, and easy to cause human errors in the verification of relay protection equipment settings with the development of the power grid, an automatic verification method of relay protection equipment settings combining cell image gray enhancement and AI recognition is studied. In this method, Gaussian mixture and particle swarm algorithm are used to enhance the gray level of the original image captured, and the binary method is used to further denoise the image; The histogram is used to segment the cells in the denoised constant value image one by one; The OCR technology in AI technology uses the maximum width backtracking segmentation algorithm to segment a coherent text in a cell into multiple single words, and collects the 13 dimensional characteristics of the text to be detected to compare with the text in the database. The text with the smallest error is the detected text, which completes the text extraction in the cell; Store the extracted text data in the database, check the data in the notification constant value sheet and the device constant value sheet, and give an abnormal prompt of different data. The experimental results show that the image pre processed by this method is clear, the fixed value single cell segmentation is accurate, and the OCR text extraction efficiency is high. Through a large number of data experiments, the final verification accuracy can reach 99.8%.
Keywords: Gray enhancement, OCR text extraction, cell segmentation, equipment constant value sheet, notify the fixed value sheet, automatic detection
DOI: 10.3233/JIFS-234457
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1503-1515, 2024
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