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

作　　者：王凌云 李开成 肖厦颖 赵晨 孟庆旭 蔡德龙 

机构地区：[1]华中科技大学电气与电子工程学院强电磁工程与新技术国家重点实验室,武汉430074

出　　处：《电测与仪表》2018年第1期14-20,33,共8页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(51277080)

摘　　要：针对复合电能质量扰动分类问题,提出了一种基于稀疏分解的分类新方法。该方法通过构建正余弦字典、脉冲字典将电能质量扰动信号分解为近似部分和细节部分,并从中提取了8个特征量。将特征向量输入改进支持向量机中可实现30种复合扰动的准确分类。基于MATLAB生成数据和真实电网数据的仿真结果表明:针对稀疏分解得到的特征向量,改进支持向量机的分类精度高于BP网络和极限学习机;文中方法对单一扰动及复合扰动均有较强的分类能力,且具有一定的抗噪声能力。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 

分 类 号：TM76[电气工程—电力系统及自动化]