Infrared Thermography Low-zero Insulator Identification Based on GWO-SVM
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摘要: 为了准确识别电网中的低零值绝缘子，提高劣化绝缘子诊断的准确率，提出了一种使用灰狼算法优化（grey wolf optimizer, GWO）与二进制支持向量机（support vector machine, SVM）分类器相结合的模型，对红外图像中的低零值绝缘子进行自动检测。首先对绝缘子红外图像进行增强，利用Ostu算法对红外图像进行分割，并对得到的二值图像进行倾斜角度矫正和切割，提取绝缘子串的有效区域，然后将图像特征用于向量机的分类识别。实验结果表明，灰狼算法优化支持向量机比常用的网格搜索算法（grid search, GS）、粒子群优化算法（particle swarm optimization, PSO）等得到的分类模型能更准确、有效地对低零值绝缘子进行识别，且准确率更高。Abstract: The accuracy of the diagnosis of degraded insulators is improved to accurately identify low-zero-value insulators in the power grid. A pair of insulator infrared images and a gray wolf optimizer (GWO) optimized binary support vector machine (SVM) classifier is proposed. Low-zero insulators are detected automatically. First, the infrared image of the insulator is enhanced; then, the infrared image is segmented using the Ostu algorithm; and the obtained binary image is subjected to tilt angle correction and cutting to extract the effective region of the insulator string. Finally, the image features are applied to the classification and recognition of vector machines. The experimental results show that the GWO-SVM can identify the low-zero insulator more accurately and effectively than the commonly used grid search (GS) and particle swarm optimization (PSO). Its rate is higher.
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Keywords:
- insulator /
- infrared imaging /
- support vector machine /
- grey wolf algorithm
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图 8 不同优化算法故障识别对比
Figure 8. Comparison of fault identification of different optimization algorithms
表 1 参数寻优方法对比
Table 1 Comparison of parameter optimization methods
Parameter optimization method Accuracy/% Optimization time/s Seeking efficiency/(s/time) Grid-search 91.523 12.693 0.2487 Random-search 92.267 8.159 0.3156 Grey wolf optimizer 95.246 6.251 0.1145 
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