基于BP神经网络的高压隔离开关分合闸监测识别

为了监视电动式高压隔离开关合闸状态,采用图像识别方法对高压隔离开关是否合闸进行监测,确保检修人员的安全。提出了一种融合NSCT和二维最大熵分割方法对图像进行分割,并提取出感兴趣区域(闸刀)。再通过像素积分投影法对闸刀分合闸情况进行特征提取,将提取到的特征值导入BP神经网络中进行训练,得出一个能够自动识别闸刀位置的分类器。将采集的图片导入BP神经网络分类器中进行识别实验论证。实验表明,处理后的图像抗噪能力强,训练出的BP神经网络对闸刀合闸状态的识别率高,达到95%以上。

Monitoring and identification of state of opening or closing isolation switch based on BP neural network

In order to monitor the closing state of the electric isolation switch, the image recognition method is used to monitor whether the isolation switch is closed to ensure the safety of the maintenance personnel. A fusion NSCT and two-dimensional maximum entropy segmentation method are proposed to segment the image, and the region of interest (gate knife) is extracted. Then, the feature extraction of the blade closing and closing is performed by the pixel integral projection method, and the extracted image will be extracted. The eigenvalues are imported into the BP neural network for training, and a classifier that can automatically identify the position of the knife is obtained. The collected pictures are imported into the BP neural network classifier for identification experiments. Experiments show that the image after processing has strong anti-noise ability, and the trained BP neural network has a high recognition rate of the closing state of the knives, which is more than 95%. This work is supported by National Natural Science Foundation of China (No. 51767006), the Key R & D Projects of Jiangxi Province (No. 20181BBE50019), Cultivate Plan for Application Research of Jiangxi Province (No. 20181BBE58015), and Science and Technology Project of Jiangxi Provincial Department of Education (No. GJJ170378).