基于迁移联邦学习的输电线路缺陷检测

Transmission Line Defect Detection Based on Transfer Federated Learning

有效检测输电线路的破损和异物对电路智能巡检至关重要.然而,由于存在着数据孤岛问题,难以收集不同电力公司的数据来训练统一的检测模型.因此,结合迁移联邦学习和目标检测算法提出了一种基于迁移联邦学习的电路缺陷检测方法.具体地,首先选用一个强大的检测模型作为基础检测模型,并冻结模型初始权重.然后通过权重矩阵的低秩分解以及插入适配器层的方式进行对不同客户端的数据进行适应学习,从而大幅降低可训练模型参数的目的.其次,提出一种权重自适应筛选方法,以精确确定模型权重层的低秩分解和适配器层的插入位置,通过简单的适应学习,即可对不同电力公司中的数据分布进行有效适应.最后,在接近真实环境的电力数据集上进行的实验验证表明,在保证客户数据安全性和隐私性的前提下,能够很好地适应不同分布的检测场景.

Effective detection of damage and foreign matter on transmission lines is very important for intelligent circuit inspection.However,it is difficult to collect data from different power companies to train a unified detection model due to the data island problem.Therefore,this study proposes a circuit defect detection method based on federated transfer learning by combining federated transfer learning and object detection algorithms.Specifically,a high-performance detection model is selected as the basic detection model,whose initial weight is frozen.The model adaptively learns from the data of different clients by using the low-rank decomposition of the weight matrix and inserting an adapter layer,so as to greatly reduce the number of the trainable parameters.An adaptive weight screening method is also proposed to accurately determine the low-rank decomposition of the weight layer and the insertion position of the adapter layer of the model.Through simple adaptive learning,the model can effectively adapt to the data distributions from different power companies.Experimental verification on a power dataset that closely resembles real-world conditions shows that the proposed model can adapt to different distributed detection scenarios under the premise of ensuring the security and privacy of customer data.