基于改进深度学习的刀闸状态识别方法研究

识别刀闸状态对于现代电力系统至关重要,传统的刀闸状态识别方法不能很好地解决多刀闸目标干扰问题。为了解决此问题,提出了一种基于改进深度学习的刀闸状态识别方法。首先采用空间加权的池化策略来改进传统的卷积神经网络(CNNs);其次利用改进CNNs在训练数据库上获得训练模型;然后通过训练模型来检测绝缘子和刀闸的潜在位置,并进一步利用非极大值抑制和直线拟合算法获得精确的绝缘子和刀闸位置;最后利用与绝缘子的连通性和刀闸区域的长宽比来识别多种刀闸的闭合或断开状态。实验结果表明此方法能够精确地定位绝缘子和刀闸的位置,显著提高刀闸状态识别的精度。

The switch state recognition method based on improved deep learning

The switch state recognition is essential for modern power systems,and the traditional switch state recognition methods cannot effectively solve the problem of multiple switch target interference.In order to solve this problem,a switch state recognition method based on the improved deep learning was proposed in this paper.Firstly,a spatially weighted pooling strategy was employed to improve traditional convolutional neural networks (CNNs).Secondly,the model was trained on the training database by using the improved CNNs.Thirdly,the trained model was used to detect the candidate positions of insulators and switches,and then,the exactly locations of insulators and switches were extracted via non-maximum suppression algorithm and line fitting method.Finally,the on/off state of switches was recognized by calculating length-width ratio of switch regions and connectivity between switch region and insulator regions.The experiment results show that the proposed method can accurately localize the insulators,switches and significantly improve the precision of recognizing switch state.