敏感设备电压暂降故障样本增广与概率评估最大熵建模  

Fault Sample Augmentation and Maximum Entropy Modeling for Probability Assessment of Sensitive Equipment Due to Voltage Sags

作　　者：郑玫 肖先勇[1] 陈韵竹[1] 郑子萱[1] 汪颖[1] ZHENG Mei;XIAO Xianyong;CHEN Yunzhu;ZHENG Zixuan;WANG Ying(College of Electrical Eng.,Sichuan Univ.,Chengdu 610065,China)

机构地区：[1]四川大学电气工程学院,四川成都610065

出　　处：《工程科学与技术》2024年第2期68-79,共12页Advanced Engineering Sciences

基　　金：国家自然科学基金项目(52077145)。

摘　　要：敏感设备电压暂降故障概率评估面临样本小和先验知识不足两大难题。本文基于自编码技术和最大熵原理,提出一种适用于小样本的设备故障概率评估随机建模方法。首先,考虑敏感设备主要对暂降幅值和持续时间特征敏感,电压耐受曲线(VTC)存在不确定性的实际情况,利用自适应Kmeans聚类算法对样本暂降幅值、持续时间聚类,在稀疏自编码器(SAE)损失函数中添加VTC不确定约束进行样本特征学习,提出基于SAE–自适应Kmeans的故障样本增广方法。其次,针对先验知识不足问题,提出基于增广样本的设备故障概率评估最大熵建模方法。最后,以个人计算机为例,在VTC概率密度函数服从均匀、正态和不同指数分布且样本数仅为5的情况下进行验证,与传统最大熵法、未引入自适应Kmeans聚类进行VTC不确定区域约束的SAE样本增广进行比较,同时与先验知识不足情况下基于主观假设的评估方法进行比较。结果表明,所提方法适用于小样本和不同分布,评估结果误差低于传统最大熵法与基于主观假设的方法,验证了稀疏自编码样本增广和最大熵建模方法对于小样本设备故障概率评估的有效性、合理性和可行性。Objective The accurate assessment of sensitive equipment fault probability caused by voltages sags is an important reference to precisely mitigate voltage sags.Currently,the fault probability assessment of sensitive equipment due to voltage sags faces two major problems:small samples and insufficient a priori knowledge.A stochastic modeling method for fault probability assessment with small samples of sensitive equipment due to voltage sag was proposed by using autoencoder technology and maximum entropy principle.Methods Firstly,the fault samples of sensitive equipment would be constrained into the same uncertainty area in voltage tolerance curve(VTC).Meanwhile,considering the fact that the sensitive equipment is mainly sensitive to the voltage sag magnitude and duration,and has the uncertainty of VTC,the adaptive Kmeans clustering algorithm was utilized to cluster the magnitudes and durations of voltage sag samples respectively to find out the center vector not only representing VTC uncertainty constraints but also neglecting the influence of samples of outlier,and then added it to the loss function of the sparse autoencoder(SAE)for better sample feature learning.The modified SAE was used to produce new samples with the input of the processed fault samples,so that a fault sample augmentation method based on SAE–Adaptive Kmeans was proposed.Secondly,in view of the problem of insufficient a priori knowledge,a maximum entropy modeling method for the fault probability assessment of sensitive equipment based on the augmented samples was proposed.Finally,taking personal computers(PCs)as examples,simulation verifications were carried out in the cases that the VTC probability density function obeys uniform distribution,normal distribution,different exponential distribution and the sample number was only 5,and the proposed method was compared with the traditional maximum entropy method and the method with SAE sample augmentation that introduced the constraints of the uncertain region of the VTC but didn’t introduce a

关 键 词：电压暂降 敏感设备 故障概率 小样本 稀疏自编码 最大熵建模 

分 类 号：TM711[电气工程—电力系统及自动化]