Abnormal Conductive State Identification of the Copper Rod in a Nickel Electrolysis Procedure Based on Infrared Image Features and Position Characteristics

Rui, Sun; Qin, G.; Li, Gaibian; Hu, Jinbao; Xiong, Jian; Xu, Huifu

doi:10.3390/app12073691

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Yao et al [ 32 ] explored the classification of insulation defects in gas-insulated switchgear and then operated SVM to identify the classification, gaining its accuracy rate of 93.75%. Sun et al [ 33 ] applied SVM to abnormal conductive faults in conductive copper rods recognition, bringing the accuracy rate up to 90%.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Optimize SVM for Insulator State Identification Based on Improved PSO Algorithm

Song

Qin

Chen

et al. 2022

Sensors

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The failure of insulators may seriously threaten the safe operation of the power system, where the state detection of high-voltage insulators is a must for the normal and safe operation of the power system. Based on the data of insulators in aerial images, this work explored an enhanced particle swarm algorithm to optimize the parameters of the support vector machine. A support vector machine model was therefore established for the identification of the normal and defective states of the insulators. This methodology works with the structure minimization principle of SVM and the characteristics of particle swarm fast optimization. First, the aerial insulator image was segmented as a target by way of the seed region growth based on double-layer cascade morphological improvements, and then, HOG features plus GLCM features were extracted as sample data. Finally, an ameliorated PSO-SVM classifier was designed to realize insulator state identification. Comparisons were made between PSO-SVM and conventional machine learning algorithms, SVM and Random Forest, and an optimization algorithm, Gray Wolf Optimization Support Vector Machine (GWO-SVM), and advanced neural network CNN. The experimental results showed that the performance of the algorithm proposed in this paper touched the top level, where the recognition accuracy rate was 92.11%, the precision rate 90%, the recall rate 94.74%, and the F1-score 92.31%.

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Section: Introductionmentioning

confidence: 99%

A New Approach to Optimize SVM for Insulator State Identification Based on Improved PSO Algorithm

Song

Qin

Chen

et al. 2022

Sensors

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Identification of electrode short circuits during the electrolytic refining of copper based on heat radiation

Bazhin

Hoang

2022

jour

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This study is aimed at improving the efficiency of monitoring and automated control over the technological state of electrolytic cells during copper refining using the recognition and identification of short circuits between the electrodes. Experimental works were performed on a laboratory bench consisting of two cells connected in series to the electrical circuit. The method of detecting short circuits is based on measuring infrared radiation using a scanning thermal imager (Оptris PI 400i / PI 450i) for determining areas with elevated temperatures of electrode and electrolyte surfaces. A short-circuit detection algorithm was developed and tested in MATLAB environment using the Image Processing Toolbox MATLAB functions. The proposed method for recognizing and identifying short circuits between the electrodes of an electrolytic cell is based on using a scanning digital thermal imager. This method allows a comprehensive assessment of the contact area, determination of both the start time of a short circuit and the degree of electrode heating in the short-circuit zone, as well as establishment of temperature values in all areas, including the electrolyte temperature in the entire volume of the cell. An algorithm for searching for short-circuit locations in an electrolytic cell is proposed. This algorithm involves the collection of data and its comparison for adjusting the inter-electrode distance, ensuring a stable energy mode for the entire electrolytic line, and determining the exact position of each cathode in the cell using the threshold processing of infrared images obtained by a thermal imager. It was established that timely identification of short circuit locations in the electrolytic bath (since the onset of a contact between the electrodes), as well as recording of the growth rates of a dendritic coalescence area and electrolyte temperature, ensure timely elimination of technological disturbances during the operation of electrolytic baths. Thus, the developed method of short circuit identification for comprehensive determination and recognition of the technological state of an electrolytic cell during the electrolytic refining of copper can be used to reach a stable energy mode with minimum deviations in the process temperature.

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Abnormal Conductive State Identification of the Copper Rod in a Nickel Electrolysis Procedure Based on Infrared Image Features and Position Characteristics

Cited by 2 publications

References 14 publications

A New Approach to Optimize SVM for Insulator State Identification Based on Improved PSO Algorithm

A New Approach to Optimize SVM for Insulator State Identification Based on Improved PSO Algorithm

Identification of electrode short circuits during the electrolytic refining of copper based on heat radiation

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