基于自主认知深度时间聚类表示的隔离开关故障诊断方法

Disconnector Fault Diagnosis Method Based on Autonomous-cognition Deep Temporal Clustering Representation

为准确识别隔离开关发生的故障,并确定故障类型,保证电网的稳定运行,提出一种基于自主认知的深度时序聚类表示模型(Autonomous-cognition deep temporal clustering representation model,AC-DTCR)对隔离开关的故障进行诊断。在数据量少且类别标签信息不可用的情况下,时间序列聚类是非常好的无监督学习技术,而AC-DTCR模型集成了时间重建和K-means目标,为提高编码器的能力,提出一种假样本生成策略和辅助分类任务,改进集群结构,获得特定于集群的时间表示。根据高压隔离开关故障模拟试验得到的电机电流数据,使用AC-DTCR模型分成四个部分对试验数据进行训练。结果表明,该模型具有良好的分类性能,与传统的分类模型和时间序列聚类模型相比,有更高的准确率,可应用于电力设备故障诊断领域中。

In order to accurately identify the fault of the disconnector,determine the fault type,and ensure the stable operation of the power grid,an autonomous-cognition deep temporal clustering representation model(AC-DTCR)is proposed to diagnose the fault of the disconnector.In the case of a small amount of data,and the class label information is not available,time series clustering is a very good unsupervised learning technology,and the AC-DTCR model integrates time reconstruction and K-means targets.In order to improve the ability of the encoder,a false sample generation strategy and auxiliary classification task are proposed to improve the cluster structure and obtain a cluster-specific time representation.According to the motor current data obtained from the fault simulation experiment of high voltage disconnector,the AC-DTCR model is divided into four parts to train the experimental data.The results show that the model has good classification performance.Compared with the traditional classification model and time series clustering model,it has higher accuracy and can be applied to the field of power equipment fault diagnosis.