基于对抗性持续学习模型的输电线路部件缺陷分类

输电线路金具巡检是电网安全态势感知中不可或缺的一部分，线路的定期巡检关系着电力系统是否能安稳运行。针对目前的输电线路部件缺陷分类模型无法处理现实情况中无限数据流的问题，该文提出一种基于对抗性持续学习的输电线路部件及其缺陷分类方法。将持续学习技术引入到输电线路部件缺陷分类任务中，使得分类模型在保证分类准确率的同时，可以从无限增长的数据流中不断学习新的分类任务，并且减少时间资源消耗。通过融入注意力机制，增强了模型对细微特征提取能力，解决了分类任务类间差异过小的问题，提高分类准确率。针对持续学习任务中的排序不可知性问题，提出基于离散度进行排序的方法，以实现持续学习分类模型的最优利用。最后，在CIFAR-100公共数据集和自建数据集上进行实验验证，并对模型的各种性能进行分析与比较。结果表明该文提出的方法实现了部件及其缺陷分类任务的可持续学习，缓解了灾难性遗忘的问题；融入注意力机制和使用L₃损失函数使分类准确率分别提高了1.43%和2.25%；实现了持续学习分类模型在已获取数据集上的最优利用，为电网安全态势感知打下了坚实的基础。

Fault Classification of Transmission Line Components Based on the Adversarial Continual Learning Model

The inspection of transmission line fittings is an indispensable part of power grid security situation awareness. Focusing on the fact that the current transmission line component defect classification model cannot handle the problem of unlimited data flow in real situations, a transmission line component and its defect classification method based on adversarial continuous learning is proposed. In this paper, continuous learning technology is introduced into the task of transmission line component defect classification, so that the classification model can continuously learn new classification tasks from the infinite growth of data stream while ensuring the classification accuracy, and reduce the consumption of time and resources. By integrating attention mechanism, the ability of the model to extract subtle features is enhanced, the problem of small difference between classification tasks is solved, and the classification accuracy is improved. Focusing on the problem of sorting unknowability in continual learning tasks, a method of sorting based on discrete degree is proposed to achieve the optimal utilization of continual learning classification model. Finally, experiments are carried out on CIFAR-100 public data set and self built data set, and various performances of the model are analyzed and compared. The results show that the proposed method realizes the sustainable learning of component and defect classification task, and alleviates the problem of catastrophic forgetting. The accuracy of classification is improved by 1.43% and 2.25% respectively by integrating attention mechanism and using L₃ loss function. The optimal utilization of continuous learning classification model is realized, which lays a solid foundation for power grid security situational awareness.