电测与仪表2018，Vol.55Issue(1)：14-20,33,8.
基于稀疏分解的复合电能质量扰动分类
Classification for multiple power quality disturbances based on sparse decomposition
摘要
Abstract
In this paper,a new classification method based on sparse decomposition is proposed to solve the problem of multiple power quality disturbance classification.Firstly,the power quality disturbance signal is decomposed into approximate part and detail part by constructing a sine cosine dictionary and a pulse dictionary.Then,8 features are extracted from the sparse decomposition results.Finally,the feature vector is inputted into the improved support vector machine,which can be used to classify the 30 kinds of complex disturbances accurately.Simulation results based on MATLAB data and real grid data show that the classification accuracy of SVM is higher than that of BP network and ELM.Besides,the classification method proposed in this paper has strong classification ability for single disturbance and complex disturbance,and has certain anti-noise performance.关键词
电能质量/扰动分类/稀疏分解/支持向量机Key words
power quality/disturbance classification/sparse decomposition/SVM分类
信息技术与安全科学引用本文复制引用
王凌云,李开成,肖厦颖,赵晨,孟庆旭,蔡德龙..基于稀疏分解的复合电能质量扰动分类[J].电测与仪表,2018,55(1):14-20,33,8.基金项目
国家自然科学基金资助项目(51277080) （51277080）
