基于DGA和稀疏化支持向量机的设备异常诊断  

Equipment Anomaly Diagnosis Based on DGA and Sparse Support Vector Machine

作　　者：潘连荣 张福泉 何井龙 杨加意 PAN Lianrong;ZHANG Fuquan;HE Jinglong;YANG Jiayi(Power Dispatching Control Center,Guangxi Power Grid Co.LTD.,Nanning 530023,China;School of Computer Science,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]广西电网有限责任公司电力调度控制中心,南宁530023 [2]北京理工大学计算机学院,北京100081

出　　处：《计算机科学》2024年第11期292-297,共6页Computer Science

基　　金：国家自然科学基金面上项目(61871204);福建省科技厅引导性项目(2018H0028);广西电网公司2023年科技项目(046000KK52222021)。

摘　　要：为了有效提高基于机器学习的设备异常诊断的精度和效率,提出了一种基于稀疏化支持向量机的故障诊断模型。首先,对异常诊断的原理和特征气体进行了分析,给出了故障类型与特征气体的关系;其次,从4个方面对数据进行预处理,包括清洗、归一化、平衡和划分;然后,针对最小二乘支持向量机普遍存在的稀疏性缺乏问题,提出将数据样本映射到高维的核空间,并通过谱聚类算法对映射后的数据进行核空间距离聚类,以实现最小二乘支持向量机的数据预处理,从而实现其稀疏化;最后,在小样本数据集上进行了具体实验分析。结果表明,对于9种类型的故障,与其他基于不同类型支持向量机的诊断模型相比,所提诊断模型仅需11次迭代就可以获得最大适应度值,平均诊断准确率为96.67%,准确率和效率均更高。In order to effectively improve the accuracy and efficiency of equipment anomaly diagnosis based on machine learning,a fault diagnosis model based on sparse support vector machine is proposed.Firstly,the principle of abnormal diagnosis and cha-racteristic gas are analyzed,and the relationship between fault types and characteristic gas is given.Secondly,the data is preprocessed from 4 aspects,including cleaning,normalization,balance and division.Then,in order to solve the problem of sparsity of least squares support vector machine,a method is proposed to map data samples to a high-dimensional kernel space,and cluster the mapped data in kernel space distance by spectral clustering algorithm,to realize the data preprocessing of least squares support vector machine,so as to realize its sparseness.Finally,the specific experimental analysis is carried out on a small sample dataset.The results show that,for 9 types of faults,compared with other diagnosis models based on different types of support vector machines,the proposed diagnosis model only needs 11 iterations to obtain the maximum fitness value,and the average diagnosis accuracy rate is 96.67%,with higher accuracy and efficiency.

关 键 词：异常诊断 机器学习 最小二乘支持向量机 油中溶解气体分析 稀疏化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]