scholarly journals Diagnosis and Classification Decision Analysis of Overheating Defects of Substation Equipment Based on Infrared Detection Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhigang Shi ◽  
Yunlong Zhao ◽  
Zhanshuang Liu ◽  
Yanan Zhang ◽  
Le Ma
Keyword(s):  
Recognition Accuracy ◽  
Infrared Image ◽  
Normal Operation ◽  
Power Equipment ◽  
Relative Temperature ◽  
Thermal Failure ◽  
Detection Technology ◽  
Diagnosis And Classification ◽  
Substation Equipment ◽  
Infrared Diagnosis

Substation equipment is not only the main part of the power grid but also the essential part to ensure the development of the national economy and People's Daily life of one of the important infrastructure. How to ensure its normal operation and find the sudden failure has become a hot issue to be solved urgently. For thermal fault diagnosis needs to classify and identify different power equipment first, this paper designed an SVM infrared image classifier, which can effectively identify three types of common power equipment. The classifier extracts HOG features from the infrared images of power equipment processed by the above segmentation and combines them with SVM multiclassification to achieve the purpose of improving the recognition accuracy. The experiment uses the classifier to identify three kinds of equipment, and the results show that the comprehensive recognition accuracy of the classifier is more than 95.3%, which is better than the traditional classification method and meets the demand for classification accuracy. In this paper, the traditional method of relative temperature difference is improved by using the temperature data of the infrared image, which can automatically judge the thermal failure level of electric power equipment. Experiments show that the diagnosis system designed in this paper can classify faults and give treatment suggestions while judging whether there are thermal faults for three types of power equipment, which verifies the feasibility and effectiveness of the substation infrared diagnosis technology designed in this paper.

scholarly journals Image Recognition Technology with Its Application in Defect Detection and Diagnosis Analysis of Substation Equipment

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Long Luo ◽  
Rukuo Ma ◽  
Yuan Li ◽  
Fangnan Yang ◽  
Zhanfei Qiu
Keyword(s):  
Recognition Accuracy ◽  
Recognition Rate ◽  
Infrared Image ◽  
Machine Learning Algorithms ◽  
Temperature Threshold ◽  
Detection Accuracy ◽  
Infrared Images ◽  
Substation Equipment ◽  
Defect Recognition ◽  
Image Defect

Detection of substation equipment can promptly and effectively discover equipment overheating defects and prevent equipment failures. Traditional manual diagnosis methods are difficult to deal with the massive infrared images generated by the autonomous inspection of substation robots and drones. At present, most of the infrared image defect recognition is based on traditional machine learning algorithms, with low recognition accuracy and poor generalization capability. Therefore, this paper develops a method for identifying infrared defects of substation equipment based on the improvement of traditional ones. First, based on the Faster RCNN, target detection is performed on 6 types of substation equipment including bushings, insulators, wires, voltage transformers, lightning rods, and circuit breakers to achieve precise positioning of the equipment. Afterwards, different classes are identified based on the sparse representation-based classification (SRC), so the actual label of the input sample can be obtained. Finally, based on the temperature threshold discriminant algorithm, defects are identified in the equipment area. The measured infrared images are used for experiments. The average detection accuracy achieved by the proposed method for the 6 types of equipment reaches 92.34%. The recognition rate of different types of equipment is 98.57%, and the defect recognition accuracy reaches 88.75%. The experimental results show the effectiveness and accuracy of the proposed method.

scholarly journals Research on Pressure-based Detection Technology for Partial Overheat Insulation Defect of Oil-less Power Equipment

2021 ◽  
Vol 632 ◽  
pp. 042009
Author(s):  
Qunying Yu ◽  
He Liu ◽  
Qiuyue He ◽  
Guozhi Zhang ◽  
Zheliang Zhang ◽  
...  
Keyword(s):  
Power Equipment ◽  
Detection Technology ◽  
Insulation Defect

Comparison of Infrared Image Pre-Processing Technique on Electronic Power Equipment

2013 ◽  
Vol 680 ◽  
pp. 339-344
Author(s):  
Hong Men ◽  
Xin Su ◽  
Peng Chen ◽  
Jia Xue Yu
Keyword(s):  
Fault Diagnosis ◽  
Target Identification ◽  
Infrared Image ◽  
Histogram Equalization ◽  
Processing Technique ◽  
Power Equipment ◽  
Low Snr ◽  
Fuzzy Image ◽  
High Pass ◽  
Subsequent Target

The disadvantages of infrared image are low resolution, bad stereoscopic sense, fuzzy image and low SNR, according to the application of infrared image in fault diagnosis of electronic power equipment, in this paper ,we make a comparative research on pre-processing technique of image de-noising and enhancement, and propose an infrared image enhancement algorithm based on platform histogram equalization combined with enhanced high-pass filtering, the algorithm can effectively improve the contrast by comparison, it is obvious to the noise effect, highlighting the objectives and details, and makes a good foundation for the subsequent target identification and fault diagnosis.

scholarly journals Research on thermal state diagnosis of substation equipment based on infrared image

2019 ◽  
Vol 11 (4) ◽  
pp. 168781401982855 ◽  
Author(s):  
Yuanbin Wang ◽  
Yang Yin ◽  
Jieying Ren
Keyword(s):  
Thermal State ◽  
Infrared Image ◽  
Substation Equipment ◽  
State Diagnosis

A Model of Weight Absolute Grey Correlation Degree for the Transformer Bushing State Assessment

2013 ◽  
Vol 345 ◽  
pp. 507-510
Author(s):  
Rong Gu ◽  
Jin Sha Yuan ◽  
Fei Lv
Keyword(s):  
Correlation Coefficients ◽  
State Assessment ◽  
The State ◽  
Condition Based Maintenance ◽  
Normal Operation ◽  
Power Equipment ◽  
Accurate Assessment ◽  
Grey Correlation ◽  
Correlation Degree ◽  
Grey Correlation Degree

Accurate assessment for the operational status of the transformer bushing is not only a prerequisite for the implementation of condition-based maintenance, but also to ensure the normal operation of the transformer and the whole power equipment conditions. In the paper, a model of weight absolute grey correlation degree was used to assess the transformer bushing state. Firstly, determined the assessment indicators and handled them. Secondly, the weights, correlation coefficients and each association were calculated. Thirdly, to compare the largest association of assessment indicators in grades as the transformer bushing final run state. Finally, give the state assessment results, according to the remark set. The result shows the model works well.

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