The transmission line conductors are an essential component of power system operation. Due to prolonged exposure to the external environment, these conductors are vulnerable to damage caused by various factors, leading to power system failures that have significant impacts on production and daily life. To monitor the condition of transmission conductors, assess the extent of damage, and ensure stable and safe grid operation, we propose an intelligent wire damage identification technology based on transmission wire damage images. Initially, we apply gray variance normalization and median filtering techniques to enhance the quality of the transmission wire image. Subsequently, local threshold segmentation is performed on the enhanced image to extract the wire area. Leveraging the characteristic that damaged areas exhibit higher gray values compared to other regions, we employ a gray value vertical and horizontal projection algorithm along with subsequent filtering processes for identifying surface damages on wires. This approach effectively transforms wire damage recognition into a data processing problem. Finally, through a series of experiments conducted in power maintenance operations, we validate its feasibility.
KEYWORDS: Optical character recognition, Feature extraction, Convolution, Power grids, Detection and tracking algorithms, Neural networks, Education and training, Mathematical optimization, Instrument modeling, Standards development
With the accelerating speed of power grid construction, the number of substations is increasing, and the management of power equipment is becoming more and more complex. The traditional manual management method is time-consuming, labor-consuming and inefficient. Due to the complex scene of power equipment nameplate, the failure of standardization of nameplate size, many characters, miscellaneous types and many steel seal information in the nameplate, the recognition accuracy of power nameplate needs to be improved. On the basis of fully mining the principle of attention mechanism encoder and decoder, combined with LSTM and convolutional neural network, this paper proposes a nameplate text recognition model based on attention mechanism to solve the problem of automatic nameplate text recognition. Experiments show that the method proposed in this paper can effectively identify the substation nameplate.
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