摘要
针对电能质量扰动特征集合冗余、分离能力差,从而导致电能质量扰动分类准确率低的问题,提出考虑多指标融合的电能质量扰动特征优选策略。首先,采用希尔伯特–黄变换提取频域特征,并构造电能质量扰动的分类特征全集作为特征子集优选对象;其次,将相交度、冗余度、分离度指标融合并构建特征子集优选规则,并通过改进布谷鸟搜索法初选得到待选特征子集;而后,将子集维度和扰动分类准确率融合并定义为代价因子,从而评估不同维度的待选特征子集的优劣,并选择代价因子最小的特征子集作为最优特征子集;最后,采用最优特征子集训练分类模型,实现电能质量扰动信号分类。经仿真对比验证,所提策略能够获取维度较小且有利于电能质量扰动分类的特征子集。
There are redundancy and poor separation ability in the power quality disturbance feature sets,which leads to the low classification accuracy of the power quality disturbance signals.Aiming at this problem,a feature selection for the power quality disturbance signals is proposed.First,the Hilbert-Huang transform is used to extract the frequency domain features,and the set of all features of power quality disturbance signals is constructed.Then,the rules of the feature subset selection are constructed based on the indexes of the intersection degree,the redundancy degree and the separation degree,and the selected feature subsets are obtained by the improved cuckoo search method.After that,the cost factor is defined based on the subset dimension and the classification accuracy of the feature subset,so as to evaluate the performance of the feature subset in different dimensions,and then the feature subset with the lowest cost factor is selected as the optimal feature subset.Finally,the optimal feature subset is used to train the classification model and classify the power quality disturbance signals.The simulation results show that the proposed feature selection has better performance in obtaining a subset of features with smaller dimensions and in classifying the power quality disturbance signals.
作者
周晨璟
邵振国
陈飞雄
张嫣
ZHOU Chenjing;SHAO Zhenguo;CHEN Feixiong;ZHANG Yan(Fujian Smart Electrical Engineering Technology Research Center(Fuzhou University),Fuzhou 350108,Fujian Province,China)
出处
《电网技术》
EI
CSCD
北大核心
2023年第9期3873-3883,共11页
Power System Technology
基金
国家自然科学基金项目(51777035)
福建自然科学基金重点项目(2020J02028)。
关键词
特征优选
多指标融合
布谷鸟搜索法
电能质量扰动分类
feature selection
multi-indicator fusion
cuckoo search
power quality disturbances classification
